This work proposes SeeExplainer, a parameter-free explainer to interpret graph neural networks, and introduces a granular-ball graph refinement mechanism that decomposes a graph into several disjoint granular-balls with no fixed size, and utilizes them as nodes to construct a structural graph.
Abstract
Instance-level explanations aim to reveal the rationale behind a model's decisions for a specific graph. Previous methods explain graph neural networks (GNNs) by selecting important edges to induce subgraphs, where edge importance is assessed by perturbing each edge and observing changes in the model predictions. However, they often neglect the synergistic effects among edges, which are crucial for accurately characterizing edge importance. To address this issue, we propose SeeExplainer, a parameter-free explainer to interpret GNNs. Specifically, we first introduce a granular-ball graph refinement mechanism that decomposes a graph into several disjoint granular-balls with no fixed size, and utilize them as nodes to construct a structural graph. This process can better capture the synergistic effects among edges. Then, we perturb nodes and edges in the structural graph to generate explanatory subgraphs based on their respective contributions. Experiments on several graph classification datasets of different networks show that SeeExplainer outperforms state-of-the-art baselines.
Transformers provide a powerful architecture for global content-based matching, but reasoning problems may benefit from a stronger inductive bias toward iterative traversal of latent relations. We introduce Graph Machine, an architecture with two explicit edge-based mechanisms: Edge-augmented attention, in which edges modulate attention between nodes, and edge-centric referral, in which nodes exchange addresses to update their edges. Conceptually, this enables the model to dynamically and differentiably construct and revise relational graphs across layers. We study this inductive bias using Sudoku under controlled settings and find that Graph Machine outperforms Transformer baselines, with ablation studies and mechanistic analysis attributing the gains to the edge mechanisms. Surprisingly, we found that the model discovers a compact edge-based construction for Sudoku geometry. Our results support explicit edge mechanisms as a promising architectural design, motivating broader evaluation.
Counterfactual explanations for graph-structured data seek to determine minimal and realistic modifications required in an input graph to alter a model's prediction to a predefined output. Although counterfactual explainers that support modifying the graph by both adding and removing edges have recently emerged, there is still a lack of general and efficient methods, especially when considering the quality of the generated explanations. Moreover, the problem remains far from solved, as existing methods exhibit different strengths and weaknesses, often trading off between explanation size, coverage and quality. For this reason, it is important to identify where each method performs well and where it falls short, so as to guide future research in the field. Thus, our study compares six state-of-the-art (SOTA) models on a diverse set of real-world and synthetic datasets, covering both binary and multi-class graph and node classification tasks, and evaluates their performance using diverse quantitative and qualitative metrics.
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This work proposes a flexible two-stage diffusion framework that combines graph coarsening with multi-step label propagation in the telecommunications domain and demonstrates that this coarsening-driven approach delivers an optimal balance between scalability, latency, and recommendation quality.
Alessandro Sbandi, F. Siciliano, Fabrizio Silvestri· arXiv.org· 0 citations
This work introduces XIGL, an architecture-agnostic human-in-the-loop strategy for removing shortcuts from GNNs, and develops an active learning strategy for prioritizing explanations that are more likely to display shortcut behavior, lowering annotation and cognitive costs.
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G EO XGNN (Local Geometry Improves Explanation Robustness for Graph Neural Networks), a robust explanation framework that explicitly models the directional sensitivity of structural perturbations from a geometric perspective that significantly improves explanation robustness over existing methods.
Mengting Diao, Li Sun, Sen Su· Proceedings of the Thirty-Fi...· 0 citations
This work-in-progress paper proposes graph edit paths as a controlled tool for probing how GNN predictions respond to structural and attribute-level edits, and develops heuristics for generating edit paths and evaluates standard GNNs along these paths, quantifying their sensitivity to different types of edits.
Florian Seiffarth· 0 citations
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