LoCoMapper is presented, a framework that takes a first step toward uncertainty-aware visualization of Mapper graphs by characterizing their local structural sensitivity to perturbations of the cover parameters by combining two node-level measures: local structural consistency and ensemble support.
This work introduces a Graph Neural Network (GNN)-based difficulty estimator that jointly captures topological and semantic aspects of violations, enabling the system to distinguish between cases suitable for automatic repair and those requiring human input.
Amedeo Pachera, Angela Bonifati, L. Lakshmanan et al.· Proceedings of the ACM on Ma...· 0 citations
Results support a conditional empirical conclusion: combining structure, topology, and stability-oriented training can improve robustness under specified structural perturbations, with dataset-dependent accuracy–stability trade-offs rather than universal guarantees.
Jelena Losic, Charles Fanning· PLOS Complex Systems· 0 citations
A structural fingerprinting framework based on graphlets: small rooted subgraphs whose distributions provide a systematic description of local-to-mesoscale topology and which challenge position graphlets as flexible structural fingerprints of complex networks, while carefully outlining their strength and weaknesses com...
Anna Pidnebesna, David Hartman, Aneta Pokorná et al.· 0 citations
The proposed Selective Hypergraph Refinement (SHR) generates candidate residual directions from the hypergraph and evaluates their reliability using graph structure, node attributes, and matched-null evidence, and shows that whether a node changes cluster is jointly governed by its native assignment gap and the directi...
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.
Topology-Aware Dynamic Reweighting (TAR+), a model-agnostic framework for node classification under strict graph distribution shifts and related robustness stressors, is proposed, supporting the broad applicability of topology-aware reweighting across heterogeneous graph learning conditions.
Jia-Xing Li, Jia-Shuo Liu, Wei-Huang Zheng et al.· IEEE Transactions on Pattern...· 0 citations
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