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Physics-Aware Random Walk Fingerprints for Scalable Power Grid Graph Classification
arXiv:2609.04943v1 [cs.LG] 4 Sep 2026
Adnan Anwar, Member, IEEE
Abstract—Cyber-physical infrastructures like power systems generate attributed graph data in which system-level outcomes depend on both network connectivity and operational interaction patterns. Recent benchmarks such as PowerGraph provide large collections of power-grid graphs for cascading-failure classification. Graph neural networks (GNNs) achieve strong predictive performance on this task, but typically require end-to-end training and model-specific tuning, while their latent representations can be difficult to relate to physically meaningful propagation patterns. Random Walk Fingerprints (RWF) offer a scalable and interpretable alternative, but existing variants primarily emphasise topology and node-level information, leaving gridrelevant operational edge states in the walk dynamics. We propose Multi-Channel Physics-Aware Random Walk Fingerprints (MC-PA-RWF) for power systems, a lightweight graph-level representation framework that introduces physical edge states into random-walk propagation. The method constructs multiple edge-weighted channels from domainrelevant attributes, extracts a channel-specific fingerprint from each weighted graph, and concatenates the resulting vectors into a compact representation. Experiments on three PowerGraph benchmark systems show substantial improvements over topology-only RWF and competitive balanced accuracy against strong GNN baselines, including Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Graph Isomorphism Networks with edge features (GINE), and Transformerbased Graph Convolutional Networks (TransformerConv). At the largest evaluated settings, the node-edge extension MC-PARWF+ achieves around 98.04% − 99.32% balanced accuracy and improves failure-class F1 over the strongest GNN baseline by 1.60 − 5.84 percentage points, with statistically significant gains across all three systems. These results show MC-PA-RWF as a scalable and interpretable approach for cyber-physical graph mining for power systems. Index Terms—power system, power system security, cascading failures, graph classification, random walk fingerprints, physicsaware features.
I. I NTRODUCTION Modern power systems generate large volumes of networked operational data from monitoring, protection, and control. In a power system, nodes represent buses, generators, loads, or substations, while edges represent transmission lines or transformers with operating states (e.g., flows, limits, and overload status) [1]. System security is therefore not determined by topology alone: it also depends on how stress, capacity limitations, and failure effects propagate through attributed electrical connections. Each operating condition can be modelled as an attributed power-grid graph and the Associate Professor A. Anwar is with the School of Information Technology and a member of the Deakin Cyber Research & Innovation Hub, Deakin University, Melbourne, Australia. (see https://experts.deakin.edu.au/50139adnan-anwar), Email: [email protected] Manuscript received September 7, 2026; revised MMM DD, YYYY.
target label can reflect system-level security or stability [2]. Such applications require graph-classification methods that are scalable, interpretable, and sensitive to power-system-relevant node and edge attributes [3]. Recent benchmarks have enabled systematic evaluation of graph-learning methods for power grid analytics. In particular, PowerGraph provides power-system graph datasets across multiple tasks and grid sizes [2]. Each operating scenario is represented using realistic topology, node features, edge features, and task-level labels, supporting problems such as power-flow prediction, optimal operation, and cascadingfailure analysis. Unlike many conventional graph-classification benchmarks from chemistry, biology, and social networks, PowerGraph highlights settings in which transmission-line operating states and other physically grounded edge attributes can directly influence system-level outcomes for power systems. Graph neural networks (GNNs) provide a natural modelling framework for these datasets. Architectures such as Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Graph Isomorphism Networks with edge features (GINE), and TransformerConv models learn graph representations by propagating information across connected components [4]–[7]. However, end-to-end neural training requires model-specific optimisation and hyperparameter tuning, while the resulting latent representations can be difficult to relate directly to physical interaction patterns. This motivates complementary graph-mining approaches that remain lightweight, reproducible, and interpretable. Random Walk Fingerprints (RWF) provide a scalable nonneural alternative [8]. RWF transforms each graph independently into a fixed-length vector by summarising multi-step random-walk connectivity within and between structurally aligned node groups. The resulting fingerprint is interpretable, compatible with conventional classifiers such as support vector machines, and scales linearly with the number of graphs. However, the standard RWF transition process is primarily topology driven. Although node attributes can be included through additional feature interactions, edge attributes do not directly shape the walk dynamics. This limitation is important for cyber-physical graphs like power system or power grid topological graphs. Two transmission lines with the same topological role may have different operational meanings because of their loading levels, residual capacities, coupling strengths, or overload conditions. Treating them as identical links in a binary adjacency matrix can remove information that is central to failure propagation. To address this gap, we propose Multi-Channel Physics-Aware Random Walk Fingerprints (MC-PA-RWF). The method represents each graph using multiple edge-weighted channels
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defined over the same topology. Each channel captures a physically meaningful interaction pattern, such as flow intensity, capacity margin, coupling strength, or overload severity of electric lines. Channel-specific RWF vectors are then extracted and concatenated into a compact graph-level representation. This extends RWF from a topology-driven graph descriptor to an edge-attributed representation that captures propagation behaviour induced by physics-aware edge states in cyberphysical power system systems. In this paper, we evaluate MC-PA-RWF on the PowerGraph graph-level cascading-failure classification task. In modern power systems, operating conditions change dynamically due to renewable generation, demand variability, feeder reconfiguration, and contingency events. Therefore, graph-level risk assessment requires models that can capture not only network topology, but also the physical state of transmission lines. The proposed method is compared with topology-only and node-feature RWF benchmark models, channel-specific PARWF variants, and benchmark neural graph models including GCN, GAT, GINE, and TransformerConv. The results show that physics-aware edge channels improve over topology-only RWF and remain competitive with strong GNN baselines while preserving interpretable power grid graph structure. The main contributions of this paper are as follows: Physics-aware random-walk fingerprints: We extend Random Walk Fingerprints [8] beyond topology and node attributes by incorporating transmission-line operating states into the random-walk transition dynamics. This enables graph-level representations to reflect power grid operating conditions, where stability and cascading-failure risk are influenced by how electrical stress propagates through physically connected network components. • Multi-channel modelling of transmission-line conditions: We propose MC-PA-RWF, a lightweight framework that represents each power grid operating point through multiple edge-weighted channels over the same network topology. Each channel encodes a power-system relationship, such as flow intensity, capacity margin, coupling strength, or overload severity. This allows random walks to follow electrically meaningful interaction strengths while preserving the scalability and interpretability of RWF. • Validation on power grid cascading-failure benchmarks: Experiments on IEEE24, IEEE39, and UK benchmark systems from PowerGraph show that the proposed method improves substantially over topology-only RWF, remains competitive with strong GNN baselines [4]–[7], and improves failure-class detection. The channel analysis further reveals which transmission-line operating state measurements contribute most strongly to cascadingfailure classification, providing interpretable insight into power grid vulnerability patterns.
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Conceptual Overview: MC-PA-RWF is motivated by the observation that power-grid operating conditions and cascading-outage risk can depend on transmission-line states and edge-state-driven propagation patterns rather than topology alone. As illustrated in Fig. 1 (a), two operating sce-
Fig. 1: Overview of the proposed MC-PA-RWF framework. narios may share the same graph structure but differ in line conditions, leading to different system-level outcomes. MC-PA-RWF captures this effect by constructing multiple physics-aware edge-weighted channels, extracting channelwise random-walk fingerprints, and concatenating them into a unified graph representation. The channels encode complementary physics-driven interaction patterns, including flow intensity, capacity margin, coupling strength, and overload severity, obtained from the edge weights. Fig. 1 (b) schematically illustrates how the multi-channel representation can improve feature-space separability over topology-only RWF [8]. Organisation of the Paper: Section II reviews related work, followed by the RWF preliminaries in Section III. Section IV presents the proposed physics-aware multi-channel random-walk fingerprint framework. Section V describes the datasets, baseline methods, and evaluation metrics, followed by the experimental results and analysis in Section VI. Finally, Section VII concludes the paper. II. R ELATED W ORK A. Graph Classification Methods Graph classification aims to assign a label to an entire graph by capturing discriminative structural and attribute patterns. Classical approaches include graph kernels, such as the Weisfeiler–Lehman kernel, neighbourhood hash kernel, GraphHopper kernel, and random-walk kernel [9]–[12]. These methods compare graphs using subtree patterns, local neighbourhood structures, shortest paths, or walk-based similarities. Although effective on conventional graph benchmarks, many
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kernel methods require pairwise graph comparisons, leading to quadratic scaling with the number of graphs. Fixed-length graph descriptors, including NetLSD, FGSD, and NetSimile, provide a more scalable alternative by summarising each graph independently using spectral, distance-based, or statistical features [13], [14], [16]. However, generic descriptors may not fully preserve the domain-specific interaction patterns that determine system-level outcomes in cyber-physical networks. B. Random-Walk-Based Graph Representations Random walks provide a natural mechanism for characterising graph connectivity because they capture how information, influence, or failure effects may propagate through a network. Traditional random-walk kernels compare pairs of graphs by counting matching walks, but their computational cost can become prohibitive for large graph collections [12]. Random Walk Fingerprints (RWF) address this limitation by transforming each graph independently into a compact fixed-length vector [8]. RWF partitions nodes into structurally aligned groups and summarises multi-step random-walk connectivity within and between these groups. The resulting representation is invariant to node ordering, interpretable at the level of node groups and walk lengths, and compatible with conventional classifiers such as support vector machines. Unlike pairwise graph kernels, RWF scales linearly with the number of graphs. Its original formulation primarily uses topologybased symmetrically normalised walk (propagation) matrices, with an optional mechanism for incorporating node-feature interactions. C. Graph Learning for Cyber-Physical Energy Systems Graph neural networks (GNNs) have become an important modelling tool for cyber-physical energy systems because power grids can be naturally represented as graphs whose nodes denote grid components, such as buses, generators, and loads, and whose edges denote physical connections, such as transmission lines. Message-passing architectures, including Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Graph Isomorphism Networks with edge features (GINE), and TransformerConv models, learn taskspecific representations by aggregating information across connected components [4]–[7]. PowerGraph [2] provides a benchmark for evaluating such models on attributed power-grid data, including power-flow, optimal-power-flow, and cascadingfailure analysis tasks. These benchmarks demonstrate the effectiveness of neural graph models for power-system analytics. However, end-to-end GNN training generally requires modelspecific optimisation and hyperparameter tuning, while the resulting latent representations can be difficult to relate directly to physically meaningful propagation patterns.
learned through end-to-end training. RWF [8] provides an attractive middle ground because it is lightweight, interpretable, and scalable. However, its standard transition dynamics are primarily topology-driven, while optional attribute enrichment focuses on node features. In cyber-physical systems, edge attributes often encode critical interaction states. For example, in a cyber-physical power grid, flow intensity, capacity margin, coupling strength, and overload severity can be represented using edge weights. These attributes should influence how random walks propagate across the network. To address this limitation, the proposed MC-PA-RWF constructs multiple physics-aware edge-weighted channels, extracts a channelspecific fingerprint from each transition process, and concatenates the resulting fingerprints into a unified graph-level representation. III. P RELIMINARIES AND BACKGROUND A. Attributed Graph Classification Let G = {G1 , . . . , GN } denote a collection of attributed graphs, where each graph is represented as Gi = (Vi , Ei , Ai , Xi , Hi , yi ). Here, Vi and Ei are the node and edge sets, Ai ∈ R|Vi |×|Vi | is the topology-based adjacency matrix, Xi ∈ R|Vi |×p is the node-feature matrix, Hi ∈ R|Ei |×q is the edge-feature matrix, and yi is the graph-level label. The objective is to learn a mapping f : Gi → yi . We follow a feature-extraction approach in which each graph is transformed into a fixed-length vector, ϕ(Gi ) = zi ∈ Rd , and a classifier is trained on the resulting representations. For cyber-physical graphs, ϕ(·) should capture not only the structural connectivity encoded by Ai , but also the operational interaction patterns encoded by Hi .
B. Random Walk Fingerprints (RWF) RWF construct an unsupervised graph-level representation by summarising random-walk connectivity over structurally aligned node groups [8]. Given an adjacency matrix A, RWF defines the symmetrically normalised walk (propagation) matrix X 1 1 Duu = Auv , T = D− 2 AD− 2 , v
D. Research Gap Existing graph-classification methods offer complementary strengths but leave an important gap for cyber-physical graph mining. Kernel methods can be expensive for large graph collections, generic graph descriptors may overlook domainspecific edge semantics, and GNN representations [4]–[7] are
where (T τ )uv captures the strength of τ -step walks from node u to node v. RWF partitions the node set into k structurally aligned groups, V = V1 ∪ · · · ∪ Vk ,
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and computes loop, within-group, and cross-group summaries for τ ∈ {1, . . . , τc }. For node groups Va and Vb , 1 X τ loopτVa = (T )uu , |Va | u∈Va X 1 (T τ )uv , walkτVa = |Va |2 u,v∈Va X 1 τ walkVa →Vb = (T τ )uv . |Va ||Vb | u∈Va v∈Vb
Collecting these statistics across walk lengths and group pairs yields a fixed-length fingerprint zG = ϕRWF (G) ∈ Rd . The representation is invariant to node ordering and compatible with graphs of different sizes. RWF can also incorporate node-feature interactions, but its transition process is primarily driven by topology [8]. C. Motivation for Physics-Aware Edge Channels Topology alone is insufficient when graph-level outcomes depend on edge states. Let huv ∈ Rq denote the feature vector of edge (u, v) ∈ E. A binary adjacency matrix records whether two nodes are connected, but it does not distinguish between lightly loaded and stressed interactions. In a power grid, for example, a line carrying high power flow with limited residual capacity should influence corresponding graph walk propagation differently from a lightly loaded line with a large remaining capacity margin. In simple terms, power flow variations across lines will influence the respective graph walk dynamics differently. This motivates an edgeattributed extension of RWF in which random-walk transitions are guided by multiple physical edge channels rather than by a single topology-based adjacency matrix. The proposed MCPA-RWF construction is introduced in Section IV. IV. P HYSICS -AWARE M ULTI -C HANNEL R ANDOM WALK F INGERPRINTS This section presents Multi-Channel Physics-Aware Random Walk Fingerprints (MC-PA-RWF), a graph-level representation for attributed cyber-physical systems. Classical RWF summarises walk patterns using a single topologybased transition matrix. In contrast, MC-PA-RWF constructs multiple physics-aware edge-weighted channels from edge attributes and extracts an RWF block from each channel. The resulting blocks are concatenated into a fixed-length graph representation. We also introduce MC-PA-RWF+, a node-edge extension that combines selected edge-channel fingerprints with an RWF-D-feature block, presented in Section IV-D. A. Problem Formulation and Overview Consider an attributed graph G = (V, E, A, X, H, y) where V and E are the node and edge sets, A is the topological adjacency matrix, X ∈ R|V |×p contains node attributes, H ∈
R|E|×q contains edge attributes, and y is the graph-level label. The goal is to construct a fixed-length fingerprint zG and train a classifier ŷ = fθ (zG ). MC-PA-RWF transforms the edge attributes into a set of weighted adjacency matrices, AG = {A(c) : c ∈ C}, where each channel c represents a distinct physical interaction pattern over the same graph support. A structural RWF fingerprint is extracted independently from each channel, and the final representation is MC zG =
(c) z , c∈C G
where ∥ denotes concatenation. Thus, MC-PA-RWF represents a graph through multiple edge-state-dependent propagation views rather than a single topology-based transition process. B. Physics-Aware Edge-Channel Construction MC-PA-RWF represents a cyber-physical graph through multiple edge-weighted views of the same topology. Each view emphasises a different physical property of the interaction between connected components. For an edge (u, v) ∈ E, let huv denote its edge-feature vector. A channel map ψc (·) converts huv into a non-negative undirected weight: ( ψc (huv ), (u, v) ∈ E, (c) (c) Auv = Avu = 0, otherwise. Thus, the topology is preserved, while the channel c modifies the walk dynamics. For the power-grid instantiation, let huv = (Puv , Quv , xuv , ruv ), where Puv and Quv are active and reactive power flows, xuv is line reactance, and ruv is the line rating. Since the proposed walk matrices are symmetric, signed or directional quantities are pconverted to magnitudes. We define apparent flow Suv = |Puv |2 + |Quv |2 , loading ratio ρuv = Suv /|ruv |, overload ouv = max(0, ρuv − 1), remaining margin muv = max(0, |ruv | − Suv ), and coupling proxy buv = 1/(|xuv | + ϵ). These quantities capture complementary operating conditions. High flow indicates heavily utilised transmission paths; a large loading ratio or small remaining margin indicates limited spare capacity; reactance-derived quantities reflect the electrical strength of a connection; and overload-related measures identify stressed lines that may contribute to cascading failures. The scalar channels are organised into four groups: FI : {|Puv |, |Quv |, Suv }, CM : {ρuv , |ruv |−1 }, CS : {|xuv |, buv }, OS : {ρuv , ouv , (muv + ϵ)−1 }. Here, FI captures power-transfer intensity, CM capacity usage, CS electrical coupling, and OS overload-related stress. Each scalar quantity defines a separate weighted view and contributes a channel-specific RWF block. To reduce scale differences and limit extreme values, each (c) scalar channel is median-scaled and clipped as Auv = (c) 1 + clip suv / med+ (s(c) ), 0, 10 , where med+ (s(c) ) is the median of positive values; if none exist, the normalised term is set to zero. The additive constant preserves edge support.
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TABLE I: Time complexity comparison of graph methods.
C. Channel-Wise Random Walk Fingerprints Present MC-PA-RWF implementation constructs undirected weighted channels. Hence, signed or directional quantities are converted to non-negative magnitudes, and each channel weight is assigned symmetrically, so A(c) ∗ uv = A(c) ∗ vu. For each channel c, this yields the symmetrically normalised walk (propagation) matrix X −1/2 (c) (c) −1/2 (c) T (c) = D(c) A D , Duu = A(c) uv . v −1/2
For zero-degree nodes, the corresponding entries of D(c) τ are set to zero. For a walk length τ ∈ {1, . . . , τc }, T (c) describes τ -step propagation under the physical interpretation of channel c. For example, FI emphasises highly loaded paths, whereas OS emphasises stressed or overloaded lines. Following RWF, nodes are partitioned into k structural groups. In our implementation, each channel induces its own degree-based ordering from the corresponding weighted graph. Let (c) (c) P (c) = {V1 , . . . , Vk } (c)
denote the resulting partition. For a group Va , the loop and within-group summaries are τ i X h 1 (c) T , ℓτ,c = a (c) uu |Va | (c) u∈Va
and waτ,c =
1
h τ i T (c)
X
(c)
|Va |2
. uv
(c)
u,v∈Va
(c)
(c)
For two distinct groups Va and Vb , the between-group summary is τ i X h 1 τ,c wa,b = (c) (c) T (c) . uv |Va ||Vb | u∈V (c) a (c)
v∈Vb
The channel fingerprint (c) zG = ϕRWF T (c) , P (c) ; τc is obtained by collecting these summaries across all groups and walk lengths. Concatenating the channel fingerprints yields MC zG . Unlike topology-only RWF, MC-PA-RWF can distinguish graphs that share the same connectivity but differ in their physical edge states. D. MC-PA-RWF+ Node-Edge Extension MC-PA-RWF models edge-channel propagation but does not include node-feature interactions inside the channel-wise extractor. MC-PA-RWF+ incorporates node-level operating information by appending an RWF-D-feature block to selected physics-aware edge-channel fingerprints: (c) MC+ D-feat zG = zG ∥ c∈C+ zG , C+ ⊆ C. For MC-PA-RWF+, the channel subset selected on the validation split is C+ = {FI, CM, OS}, yielding (FI)
(CM)
MC+ D-feat zG = zG ∥zG ∥zG
(OS)
∥zG
.
Method
Time complexity
Random walk kernel [12] NetLSD [13] FGSD [14] NetSimile [16]
O(N 2 n3 ) O(N rm + N r2 n) O(N n2 ) O(N f (n + n log n))
RWF-D [8] RWF-D-feature [8] MC-PA-RWF (Proposed) MC-PA-RWF+ (Proposed)
O(N τc mn) O(N τc (mn + pn2 )) O(N Cτc mn) O(N τc ((C+ + 1)mn + pn2 ))
Note: Here, N denotes the number of graphs, r the number of eigenvalues retained for the NetLSD approximation, and f the number of node-level structural features used by NetSimile. D-feat The node-feature block zG is computed once using the RWF-D-feature mechanism [8]. The selected edge-channel blocks contribute structural summaries over their weighted walk (propagation) matrices. This modular design combines local node-feature evidence with edge-state information without duplicating node-feature interactions across channels. Both MC-PA-RWF and MC-PA-RWF+ produce fixed-length graph-level vectors. In this work, an SVM is trained on the extracted fingerprints, separating physics-aware feature extraction from supervised classification.
E. Complexity Analysis Table I compares the feature-extraction costs of the proposed methods with representative graph-representation approaches. Let N denote the number of graphs, n and m the maximum numbers of nodes and edges per graph, τc the maximum walk length, p the node-feature dimension, C = |C| the number of physical channels, and C+ = |C+ | the number of channels used by MC-PA-RWF+. Under the sparse RWF assumptions of [8], topology-only RWF-D has complexity O(N τc mn), while RWF-D-feature requires O N τc (mn + pn2 ) due to additional node-feature interactions. MC-PA-RWF applies structural RWF extraction independently to C edge-weighted symmetrically normalised walk matrices, resulting in O(N Cτc mn). Constructing the weighted adjacency matrices costs O(N Cm), while channelwise degree-based partitioning costs O(N C(m + n log n)). These are lower order than repeated fingerprint extraction. MC-PA-RWF+ combines one RWF-D-feature block with C+ selected edge-channel blocks, giving O N τc (C+ + 1)mn + pn2 . Both proposed variants remain linear in the number of graph instances N . Their dependence on the size of each graph is inherited from the underlying RWF computation, while the additional overhead is controlled by the small, fixed number of physical channels. F. Algorithm Algorithm 1 summarises both MC-PA-RWF and MC-PARWF+. Both variants extract structural RWF fingerprints from physics-aware edge-weighted channels. MC-PA-RWF uses all physical channels in C and concatenates only the resulting edge-channel fingerprints. MC-PA-RWF+ additionD-feat ally includes an RWF-D-feature block zG , computed from i
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Algorithm 1: MC-PA-RWF and MC-PA-RWF+ Feature Extraction Input: Graph dataset G = {Gi }N i=1 , where Gi = (Vi , Ei , Ai , Xi , Hi , yi ); physical channel set C; selected channel set C+ ⊆ C for MC-PA-RWF+; edge-channel maps {ψc }c∈C ; number of node groups k; maximum walk length τc ; mode η ∈ {MC-PA-RWF, MC-PA-RWF+}. Output: Graph fingerprints {zGi }N i=1 . 1 foreach Gi ∈ G do 2 Initialise Zi ← [ ]; 3 if η = MC-PA-RWF+ then 4 Compute node-feature RWF block D-feat zG = RWFfeat (Ai , Xi ; k, τc ); i D-feat to Zi ; 5 Append zG i 6 Set active channel set C ⋆ ← C+ ; 7 8 9 10
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12
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else Set active channel set C ⋆ ← C; foreach c ∈ C ⋆ do Construct physical channel adjacency (c) Ai,uv = ψc (hi,uv ) for (u, v) ∈ Ei ; (c) Normalise Ai as a non-negative weighted graph; (c) Partition nodes into k RWF groups Pi using channel-weighted degrees; (c) (c) −1/2 (c) (c) −1/2 Ai Di ; Compute Ti = Di
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Extract structural RWF summaries (c) (c) (c) zGi = RWFstr (Ti , Pi ; τc ); (c) Append zGi to Zi ;
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Concatenate selected blocks: zGi = ∥z∈Zi z;
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return {zGi }N i=1 ;
TABLE II: PowerGraph graph-level classification datasets [2]. Dataset Nodes Edges Graphs Used Classes IEEE24 24 38 21,500 1k–21.5k Stable / Failure-prone IEEE39 39 46 28,000 1k–28k Stable / Failure-prone UK 29 99 64,000 5k–64k Stable / Failure-prone
topology and node attributes, and then appends selected edgechannel fingerprints from C+ . Thus, node-feature interactions are included only through the RWF-D-feature block, while the physical channels use structural RWF summaries over weighted walk matrices. V. E XPERIMENTAL S ETUP This section describes the datasets, baseline methods used for comparison, and the evaluation metrics used to assess MCPA-RWF and MC-PA-RWF+. The experiments evaluate graphlevel cascading-failure classification on PowerGraph benchmark systems under increasing dataset sizes. We compare the proposed physics-aware random-walk fingerprints with original RWF variants and supervised GNN baselines, using
balanced accuracy as the primary metric and failure-class F1 to assess how reliably each method identifies failure-prone operating scenarios. A. Dataset and Task We evaluate the proposed method on the graph-level cascading-failure classification benchmark from PowerGraph [2]1 . PowerGraph provides attributed graph datasets for cyber-physical energy-system learning tasks, including powerflow analysis, optimal-power-flow analysis, and cascadingfailure prediction. In the graph-level cascading-failure setting, each graph represents one operating scenario of a power-grid system associated with an initial triggering outage. The graphlevel label is derived from the final demand not served (DNS) produced by the physics-based cascading-failure simulation. In this work, we use the binary classification setting. A graph is labelled as stable if the cascading-failure simulation results in no demand not served, and failure-prone otherwise: ( 0, if DNSi = 0, yi = 1, if DNSi > 0. Here, yi = 0 denotes Stable/Class 0 and yi = 1 denotes Failure-prone/Class 1. Each graph is represented as Gi = (Vi , Ei , Ai , Xi , Hi , yi ), where nodes correspond to grid components, edges correspond to physical connections, Ai is the topology-based adjacency matrix, Xi denotes node attributes, and Hi denotes edge attributes. The proposed MC-PA-RWF uses the topology and edge attributes to construct physical edge-weighted channels representing complementary line semantics, including flow intensity, capacity margin, coupling strength, and overload severity. Node attributes are not used inside the MC-PA-RWF edge-channel extractor. They are used in RWF-D-feature and in the proposed MC-PA-RWF+ extension, where an RWF-Dfeature block computed from topology and node attributes is appended to selected physics-aware edge-channel fingerprints. Thus, MC-PA-RWF evaluates edge-attributed physical propagation, while MC-PA-RWF+ combines node-feature evidence with physics-aware edge-channel random-walk summaries. We use only input graph attributes and graph-level labels for training and evaluation; ground-truth explanation files, such as failed-edge explanation labels, are not used as input features. Table II summarises the benchmark systems and graph sizes used in the experiments. B. Benchmark Methods We compare MC-PA-RWF with three groups of methods. RWF baselines: We include two direct RWF baselines. RWF-D is the topology-only RWF variant using degreebased structural-role partitioning [8]. It computes randomwalk fingerprints from the topology-based adjacency matrix only. RWF-D-feature extends RWF-D by incorporating node attributes through the node-feature aggregation mechanism of 1 https://figshare.com/articles/dataset/PowerGraph/22820534
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TABLE III: IEEE24 bus test system balanced accuracy (%) across different graph sizes Family
Method
RWF (Original)
RWF-D [8] RWF-D-feature [8]
71.78 ± 4.15 72.29 ± 4.12 66.75 ± 1.22 67.51 ± 1.77 67.06 ± 1.26 67.04 ± 0.74 93.00 ± 5.70 96.09 ± 1.86 96.33 ± 1.59 97.02 ± 1.11 97.50 ± 1.00 97.81 ± 0.45
1k
3k
GNN
GCN [4] GAT [5] GINE [6] TransformerConv [7]
75.53 ± 2.66 76.98 ± 2.06 83.38 ± 4.54 88.43 ± 3.20
85.01 ± 4.50 88.50 ± 1.95 93.45 ± 2.74 94.42 ± 1.96
89.04 ± 1.54 90.29 ± 1.38 92.51 ± 2.81 95.23 ± 1.93
92.80 ± 0.85 91.96 ± 1.47 96.82 ± 0.62 97.59 ± 0.67
93.90 ± 1.14 93.34 ± 1.99 97.78 ± 0.51 98.06 ± 1.08
95.23 ± 1.25 92.90 ± 2.06 97.84 ± 0.82 98.76 ± 0.49
Physics-aware RWF Variants
PA-RWF-FI PA-RWF-CM PA-RWF-CS PA-RWF-OS MC-PA-RWF (Proposed) MC-PA-RWF+ (Proposed)
92.45 ± 3.93 91.15 ± 5.08 70.58 ± 4.78 94.85 ± 2.74 94.51 ± 4.23 93.54 ± 6.90
96.79 ± 1.44 96.10 ± 1.12 72.17 ± 3.94 96.42 ± 1.25 97.08 ± 0.95 96.47 ± 1.27
96.38 ± 1.14 96.63 ± 1.67 66.93 ± 2.75 96.48 ± 1.59 97.68 ± 1.68 97.48 ± 1.41
97.37 ± 0.70 98.09 ± 0.69 67.04 ± 1.70 98.12 ± 0.43 98.43 ± 0.29 98.17 ± 0.16
98.53 ± 0.64 98.63 ± 0.49 66.72 ± 1.65 98.65 ± 0.28 98.71 ± 0.50 99.00 ± 0.30
98.77 ± 0.48 98.95 ± 0.33 67.62 ± 0.91 98.60 ± 0.35 99.15 ± 0.22 99.21 ± 0.36
RWF [8]. These baselines allow us to evaluate whether edgechannel fingerprints provide additional discriminative information beyond topology and node attributes. Physics-aware RWF variants (Proposed): We evaluate two proposed physics-aware RWF variants. MC-PA-RWF constructs multiple physical edge-weighted graph channels and concatenates the structural RWF fingerprints extracted from these channels. This variant uses topology and edge attributes only. MC-PA-RWF+ extends MC-PA-RWF by appending an RWF-D-feature block computed from topology and node attributes to selected physics-aware edge-channel fingerprints. Thus, MC-PA-RWF evaluates multi-channel edgeattributed random-walk propagation, while MC-PA-RWF+ evaluates the combined effect of node-feature evidence and physics-aware edge-channel propagation. Further details are provided in Section IV. To analyse the contribution of individual physical edge channels, we include four single-channel diagnostic variants: PA-RWF-FI, PA-RWF-CM, PA-RWF-CS, and PA-RWF-OS. Each variant follows the MC-PA-RWF fingerprint-extraction methodology but uses only one edgeweighted channel: flow intensity, capacity margin, coupling strength, or overload severity, respectively. These variants are included for channel-level analysis of the proposed models. Neural graph baselines: We compare against four supervised GNN baselines for graph-level classification: GCN [4], GAT [5], GINE [6], and TransformerConv [7]. GCN provides a standard message-passing baseline based on neighbourhood feature aggregation, while GAT uses attention weights to learn the relative importance of neighbouring nodes. GINE extends the expressive GIN architecture by incorporating edge attributes into the message-passing update, making it a strong baseline for attributed power-grid graphs. TransformerConv uses attention-based graph convolution and provides a more flexible neural baseline for modelling graph interactions. All GNNs use the same graph-level labels and are evaluated with the same performance metric as the RWF-based methods. C. Evaluation Metrics and Implementation Balanced accuracy is used as the primary metric because the stable and failure-prone classes are imbalanced. Balanced TP TN accuracy is defined as BAcc = 21 TP+FN + TN+FP . We report the mean and standard deviation across repeated random seeds. At the largest evaluated size of each benchmark,
5k
10k
15k
21.5k
we additionally report failure-class F1 to assess detection of the safety-critical failure-prone class. For each split, preprocessing and model selection are performed using training and validation data only. For the RWFbased methods, graph fingerprints are first extracted and then classified using an SVM. SVM is widely used for classification tasks [15]. The number of node groups k, maximum walk length τc , SVM hyperparameters, and GNN hyperparameters are selected from fixed search spaces using the validation split. Feature standardisation is fitted on the training split and applied unchanged to the validation and test splits. The test split is used only for final evaluation. The GNN baselines (GCN [4], GAT [5], GINE [6], and TransformerConv [7]) are evaluated using the same graph-level labels and balanced-accuracy metric. For reproduced comparisons, the same train–validation–test splits and random seeds are used across all methods. GNN training uses validationbased early stopping. Statistical comparisons are conducted across paired random seeds using a two-sided paired t-test at the 0.05 significance level. VI. R ESULTS AND D ISCUSSION This section evaluates the proposed Physics-aware RWF framework (MC-PA-RWF and MC-PA-RWF+) through the following research questions: • RQ1: Do physics-aware edge channels improve graphlevel power grid cascading-failure classification compared with topology-only and node-feature RWF baselines? • RQ2: Does combining complementary physical edge channels produce a more robust power grid graph representation than relying on a single channel or topology alone for such powerful RWF algorithms? • RQ3: How do MC-PA-RWF and MC-PA-RWF+ compare with strong neural graph baselines (heavily used in power system) in terms of balanced accuracy and failure-class detection? The results provide three main findings. (i) First, topologyonly RWF suffers heavily for cascading-failure classification, particularly on the larger UK system. (ii) Second, operational edge channels such as flow intensity, capacity margin, and overload severity provide strong discriminative information, while their multi-channel combination improves robustness
Balanced accuracy (%)
8
100 90 80 GCN GAT GINE TransformerConv RWF-D RWF-D-feature MC-PA-RWF MC-PA-RWF+
70 60 50 1
3
5
10
15
GCN GAT GINE TransformerConv RWF-D RWF-D-feature RWF-D-feature-bal. MC-PA-RWF MC-PA-RWF+
GCN GAT GINE TransformerConv RWF-D RWF-D-feature MC-PA-RWF MC-PA-RWF+
21.5
1 3 5
10
20
28
5 10
20
64
Graphs used (thousands)
Graphs used (thousands)
Graphs used (thousands)
(a) IEEE24
(b) IEEE39
(c) UK
Fig. 2: Classification performance across dataset sizes for IEEE24, IEEE39, and UK benchmark test systems. TABLE IV: IEEE39 bus test system balanced accuracy (%) across different graph sizes. Family
Method
1k
RWF (Original)
RWF-D [8] RWF-D-feature [8]
GNN
GCN [4] 76.67 ± 9.20 GAT [5] 78.22 ± 6.09 GINE [6] 86.75 ± 9.39 TransformerConv [7] 77.63 ± 11.17
Physics-aware RWF (Proposed)
MC-PA-RWF MC-PA-RWF+
3k
5k
10k
20k
28k
62.43 ± 7.51 69.88 ± 3.98 73.28 ± 3.16 73.65 ± 7.96 92.03 ± 3.35 95.74 ± 2.70
69.51 ± 2.54 98.12 ± 0.83
71.23 ± 1.86 97.66 ± 0.72
71.96 ± 1.48 98.15 ± 0.37
89.05 ± 2.50 89.01 ± 1.86 96.56 ± 1.52 94.36 ± 2.98
91.04 ± 1.69 90.13 ± 2.37 97.87 ± 0.55 97.17 ± 1.07
93.96 ± 1.30 90.48 ± 0.96 97.41 ± 0.80 97.76 ± 0.81
82.69 ± 3.50 83.09 ± 4.33 91.75 ± 2.49 88.25 ± 6.94
84.07 ± 3.37 84.56 ± 3.86 94.33 ± 1.31 90.76 ± 6.27
74.54 ± 4.11 93.32 ± 5.69 94.99 ± 2.76 96.35 ± 0.99 97.30 ± 1.02† 97.15 ± 0.48 77.93 ± 10.89 93.08 ± 6.53 97.80 ± 2.78 96.92 ± 1.19† 97.39 ± 0.89† 98.04 ± 0.54†
Note: Bold values indicate the highest mean. †: not significantly different from the best method in the column under a paired two-sided t-test (α = 0.05).
TABLE V: IEEE39 fusion results: balanced accuracy (%) Method
1k
3k
5k
10k
20k
28k
RWF-D-feature 82.05 93.99 97.22 98.44 98.51 98.63 [8] ± 6.76 ± 3.71 ± 1.70 ± 0.48 ± 0.34 ± 0.38 MC-PA-RWF (Adpt.)
87.19 95.46 97.84 98.50 98.92 98.91 ± 7.16 ± 3.66 ± 2.57 ± 0.51 ± 0.39 ± 0.40
across dataset sizes. (iii) Finally, MC-PA-RWF+ is competitive with strong GNN baselines in balanced accuracy and significantly improves failure-class F1 on all three systems at the largest evaluated dataset sizes. In the results tables for this study, bold values indicate the highest mean balanced accuracy for each dataset size. A dagger (†) indicates that the proposed method performs comparably to the best method in the same column, with no statistically significant difference under a paired two-sided t-test at the 0.05 level. A. RQ1: Do Physics-Aware RWF Improve Classification? Tables III–VI and Fig. 2 show that topology alone does not provide a sufficiently informative representation for cascadingfailure prediction. The topology-only RWF-D baseline reaches only 67.04% balanced accuracy on IEEE24 at 21.5k graphs and 71.96% on IEEE39 at 28k graphs. On the UK benchmark, RWF-D reaches only 50.77% at 64k graphs, remaining close to the 50% balanced-accuracy baseline. This behaviour is expected because a binary adjacency matrix indicates whether two buses are connected but does not capture whether a transmission line is lightly loaded, close to its capacity limit, or part of a stressed propagation pathway. The node-feature
baseline RWF-D-feature confirms that operating attributes are important. At the largest evaluated sizes, it improves balanced accuracy to 97.81% on IEEE24 and 98.15% on IEEE39. However, its performance is system-dependent. On the UK benchmark, RWF-D-feature achieves only 51.79% balanced accuracy at 64k graphs. Even an additional diagnostic variant, RWF-D-feature-balanced, which uses the same RWF-Dfeature representation but trains the SVM with class-balanced weights, reaches only 58.54% at 64k graphs for the UK dataset of Table VI. This contrast indicates that node-feature evidence alone does not generalise consistently across power-system settings. The proposed physics-aware representation addresses this limitation by allowing edge states to shape the randomwalk transition dynamics. At the largest evaluated sizes, MCPA-RWF reaches 99.15% on IEEE24, 97.15% on IEEE39, and 99.14% on the UK system. The node-edge extension MC-PARWF+ reaches 99.21%, 98.04%, and 99.32%, respectively. The improvement is particularly clear on the UK benchmark, where the proposed methods remain highly accurate while the original RWF variants remain close to the 50% balancedaccuracy baseline. These findings answer RQ1 positively: physics-aware edge channels provide substantial additional information beyond topology-only RWF and improve robustness when node-feature evidence alone is insufficient. B. RQ2: Does Multi-Channel Fusion Improve Robustness? To assess the contribution of each physical edge channel, we first evaluate four single-channel diagnostic variants on the IEEE24 benchmark: PA-RWF-FI, PA-RWF-CM, PA-RWFCS, and PA-RWF-OS. At 21.5k graphs, PA-RWF-FI reaches
9
TABLE VI: UK test system balanced accuracy (%) across different graph sizes. Family
Method
5k
10k
20k
64k
RWF (Original)
RWF-D [8] RWF-D-feature [8] RWF-D-feature-balanced
50.00 ± 0.00 50.00 ± 0.00 50.10 ± 4.85
50.30 ± 0.68 50.00 ± 0.00 52.84 ± 5.25
50.00 ± 0.00 50.14 ± 0.30 57.51 ± 4.41
50.77 ± 0.47 51.79 ± 0.27 58.54 ± 1.94
GNN
GCN [4] GAT [5] GINE [6] TransformerConv [7]
91.46 ± 8.17 93.28 ± 7.01 94.87 ± 3.72 95.79 ± 4.19
94.39 ± 1.75 90.71 ± 15.25 97.16 ± 2.16 98.01 ± 1.13
95.10 ± 1.70 95.09 ± 1.30 96.37 ± 1.87 97.85 ± 2.16
96.43 ± 1.25 98.62 ± 0.61 99.36 ± 0.30 99.63 ± 0.43
Physics-aware RWF (Proposed)
MC-PA-RWF MC-PA-RWF+
97.59 ± 1.24 97.00 ± 2.86†
95.74 ± 3.28† 96.94 ± 3.56†
98.27 ± 1.09† 98.99 ± 0.38
99.14 ± 0.59† 99.32 ± 0.35†
Note: Bold values indicate the highest mean. †: not significantly different from the best method in the column under a paired two-sided t-test (α = 0.05).
TABLE VII: Failure Class comparison against the best GNNs Dataset
Size
Best GNN GNN F1 Prop. F1 p-value Stat. Sig.
IEEE24 [2] 21.5k TransConv IEEE39 [2] 28k TransConv UK [2] 64k TransConv
97.24% 91.03% 96.95%
98.84% 96.87% 99.05%
0.0015 0.0024 0.0147
Yes Yes Yes
Note: Prop. F1: MC-PA-RWF+ failure-class F1. Stat. Sig.: significant gain over the best GNN under a paired two-sided t-test (p < 0.05).
98.77% balanced accuracy, PA-RWF-CM reaches 98.95%, and PA-RWF-OS reaches 98.60%. In contrast, PA-RWF-CS reaches only 67.62%, remaining close to the topology-only RWF-D baseline. This difference is physically meaningful. Flow intensity, capacity margin, and overload severity reflect the operating condition of transmission lines under a scenario and are therefore closely related to how cascading failures spread through the network. Coupling strength captures an important electrical property, but it is comparatively static and less discriminative when used alone. The single-channel analysis therefore provides interpretable evidence that the channels contribute differently to cascading-failure classification. The proposed MC-PA-RWF combines these complementary edge views into a unified fingerprint. On IEEE24, MC-PARWF achieves the highest mean balanced accuracy at 3k, 5k, and 10k graphs, reaching 97.08%, 97.68%, and 98.43%, respectively. MC-PA-RWF+ becomes the strongest method at 15k and 21.5k graphs, reaching 99.00% and 99.21%. At 21.5k graphs, MC-PA-RWF+ improves over the strongest singlechannel variant, PA-RWF-CM, by 0.26 percentage points. As an additional diagnostic comparison, we report the IEEE39 score-level fusion variant denoted as MC-PA-RWF (Adpt.). This variant combines calibrated SVM outputs from the RWF-D-feature and the physics-aware edge-channel representation (MC-PA-RWF). It is not treated as a separate proposed method; rather, it is included to test whether nodefeature and edge-channel fingerprints provide complementary decision evidence. Table V reports this calibrated score-level fusion experiment under its own ten-seed evaluation scenario (its values should therefore not be directly compared with those in Table IV). Across the evaluation, MC-PA-RWF (Adpt.) improves over RWF-D-feature at every dataset size. Balanced accuracy increases from 82.05% to 87.19% at 1k graphs and from 98.63% to 98.91% at 28k graphs. The smaller gain at larger sizes is expected as performance approaches saturation.
These results answer RQ2 positively. No single edge channel is sufficient across all settings. Combining complementary physical channels improves the robustness of the graph representation, while the node-edge extension provides additional benefit when both local node states and line-level propagation patterns are informative. C. RQ3: How Do the Proposed Methods Compare with GNN Baselines? The proposed methods are competitive with strong supervised GNN baselines across the three systems. At the largest IEEE24 setting, TransformerConv is the strongest GNN with 98.76% balanced accuracy, whereas MC-PA-RWF+ reaches 99.21%, an improvement of 0.45 percentage points. On IEEE39 at 28k graphs, TransformerConv reaches 97.76%, while MC-PA-RWF+ reaches 98.04%, an improvement of 0.28 percentage points. On the larger UK benchmark at 64k graphs, TransformerConv achieves the highest mean balanced accuracy of 99.63%, while MC-PA-RWF+ remains close at 99.32%. The difference is not statistically significant under the paired test. The dataset-size results provide a more detailed view. On IEEE39, MC-PA-RWF is the best-performing method at 3k graphs with 93.32%, while MC-PA-RWF+ is best at 5k graphs with 97.80%. At 20k and 28k graphs, several proposed-method results are dagger-marked, indicating that their differences from the strongest method are not statistically significant. On the UK benchmark, MC-PA-RWF is best at 5k graphs with 97.59%, and MC-PA-RWF+ is best at 20k graphs with 98.99%. At 64k graphs, MC-PA-RWF+ remains statistically comparable to TransformerConv. These results show that a non-neural, physics-aware graph fingerprint can match or exceed strong message-passing architectures in several regimes while remaining more transparent. Unlike GNN embeddings, MC-PA-RWF features are fixed, explicit, and traceable to a physical channel, walk length, and withinor cross-group walk pattern. Thus, the proposed method offers competitive accuracy without relying on fully latent learned graph representations. 1) Failure-Class (F1) Detection: Balanced accuracy summarises performance across both classes, but failure-class F1 provides a more application-relevant safety measure because the failure-prone class corresponds to operating scenarios that may lead to cascading outages. In this setting, missing a failure-prone graph is more consequential than misclassifying
10
a stable one, so we separately evaluate how well each method identifies this critical class. Table VII compares MC-PA-RWF+ against the strongest GNN baseline at the largest evaluated dataset size for each system. On IEEE24, failure-class F1 improves from 97.24% for TransformerConv to 98.84% for MC-PA-RWF+, a gain of 1.60 percentage points (p = 0.0015). On IEEE39, F1 improves from 91.03% to 96.87%, a gain of 5.84 percentage points (p = 0.0024). On the UK benchmark, it improves from 96.95% to 99.05%, a gain of 2.10 percentage points (p = 0.0147). All three improvements are statistically significant under paired two-sided t-tests at the 0.05 level. The IEEE39 improvement is especially important. Although the strongest methods are close in balanced accuracy, MCPA-RWF+ identifies failure-prone operating scenarios more effectively. This shows why F1 class-level metrics should be considered alongside balanced accuracy. VII. C ONCLUSION This work demonstrates that physically meaningful edge states provide essential information for graph-level classification tasks for cyber-physical energy systems. Across the IEEE24, IEEE39, and UK power system datasets (PowerGraph benchmarks), the proposed MC-PA-RWF variants consistently improve over topology-only RWF and remain effective in settings where node-feature RWF does not generalise well. At the largest evaluated dataset sizes, MC-PA-RWF+ achieves balanced accuracies between 98.04% and 99.32%, matching or exceeding strong GNN baselines across the three systems. More importantly, it improves failure-class F1 over the best GNN baseline by 1.60–5.84 percentage points, with statistically significant gains on every benchmark. These results indicate that explicitly modelling edge-state-dependent propagation improves not only overall classification performance but also the detection of safety-critical failure-prone scenarios. The channel-level analysis further shows that dynamic edge attributes reflecting changing operating conditions are more discriminative than static interaction properties alone. This confirms that MC-PA-RWF captures interpretable failurerelated patterns from edge-level cyber-physical behaviour. MC-PA-RWF therefore offers a scalable and transparent alternative to fully latent graph representations while preserving linear complexity with respect to the number of graph instances. Future work will investigate adaptive channel selection and extend the framework to other cyber-physical domains with application-specific edge information. VIII. ACKNOWLDGEMENT This work is supported by the Deakin Mini ARC Analog Program. ChatGPT-5 is used to assist with language editing. All conceptual content and technical contributions are solely of the author. R EFERENCES [1] A. Anwar, A. N. Mahmood, Z. Tari, and A. Kalam, “Measurementdriven blind topology estimation for sparse data injection attack in energy system,” Electric Power Systems Research, vol. 202, p. 107593, 2022.
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