SynCo is a synthetic graph generator that lets users control node degree distributions and sub‑community structures, addressing limitations of existing generators that rely on power‑law distributions and lack flexibility. It is evaluated on graph mimicking, hyperparameter tuning, and node clustering, outperforming state‑of‑the‑art methods while preserving original data distributions. SynCo can generate large graphs with up to 2.1 million nodes.
By Guilherme Henrique Messias, Mariana Caravanti de Souza, Sylvia Iasulaitis, Alan Dem\'etrius Baria Valejo
The paper introduces SEMGNN, an end‑to‑end self‑explainable multi‑label graph neural network that simultaneously classifies nodes and identifies edges contributing to each predicted label. Unlike post‑hoc explainers, SEMGNN jointly learns a predictor and a sparse edge‑mask explainer, leveraging label‑label correlations to improve classification and generate distinct, coherent explanations for each label. Experiments on synthetic and real‑world networks in social, entertainment, and life‑science domains demonstrate competitive predictive performance and more faithful, compact label‑conditioned explanations.
By Yingqi Feng, Yufei Tang, Min Shi, Xingquan Zhu
arXiv:2602.09716v2 Announce Type: replace
Abstract: Computing the importance of nodes in networks is a long-standing fundamental problem that has driven extensive study of various centrality measures...
By Justin Dachille, Aurora Rossi, Sunil Kumar Maurya, Frederik Mallmann-Trenn, Xin Liu, Fr\'ed\'eric Giroire, Tsuyoshi Murata, Emanuele Natale
arXiv:2605. 15511v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning.
By Louisa Cornelis, Johan Mathe, Louis Van Langendonck, Guillermo Bern\'ardez, Nina Miolane
arXiv:2607. 00671v1 Announce Type: cross Abstract: Graphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role.
By Yifei Sun, Zemin Liu, Bryan Hooi, Yang Yang, Rizal Fathony, Jia Chen, Bingsheng He
HERALD is a new gradient‑free graph condensation framework that adapts node scoring and feature selection to a graph’s heterophily level. It selects features using a joint Fisher‑discriminability and activation‑density criterion, and scores nodes with a weighted combination of prototype representativeness, decision‑boundary proximity, and Local Intrinsic Dimensionality, where the weights depend on the heterophily ratio. The selected nodes are assembled into a condensed subgraph via score‑ordered BFS expansion, Personalized PageRank pruning, and class rebalancing, achieving comparable storage to BONSAI and outperforming state‑of‑the‑art condensers on heterophilic graphs while remaining competitive on homophilic ones across multiple GNN architectures.
By Sujan Chakraborty, Priyanka Saha, Saptarshi Bej
arXiv:2603. 06952v2 Announce Type: replace Abstract: As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentially growing neighborhoods.
By Yuhang Song, Naima Abrar Shami, Romaric Duvignau, Vasiliki Kalavri
HeTGB is a new benchmark for heterophilic text‑attributed graphs, consisting of five real‑world datasets where nodes have rich textual descriptions. It allows systematic evaluation of graph neural networks, pre‑trained language models, and co‑training methods on node classification. The benchmark highlights the utility of text attributes, the challenges of heterophilic TAGs, and the limitations of current models.
By Shujie Li, Yuxia Wu, Yuan Fang, Chuan Shi
arXiv:2607. 03587v1 Announce Type: new Abstract: We propose NetinfoGC, a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning.
By Abdullah Shaik, Anwar Said
arXiv:2505.11298v2 Announce Type: replace
Abstract: Graph Neural Networks (GNNs) are powerful tools for learning on structured data, yet the relationship between their expressivity and predictive per...
By Sohir Maskey, Raffaele Paolino, Fabian Jogl, Gitta Kutyniok, Johannes F. Lutzeyer
arXiv:2601. 17469v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have shown remarkable capabilities in learning from graph-structured data with various applications such as social analysis and bioinformatics.
By Wei Ju, Wei Zhang, Siyu Yi, Zhengyang Mao, Yifan Wang, Jingyang Yuan, Zhiping Xiao, Ziyue Qiao, Ming Zhang
arXiv:2609. 19210v1 Announce Type: cross Abstract: Graph neural networks are widely used for transductive node classification, with accuracy typically measured on randomly drawn train/validation/test splits.
By Naga Venkata Sai Jitin Jami, Thomas Altstidl, Sebastian Hoefler, Jonas Mueller, Dario Zanca, Bjoern Eskofier, Heike Leutheuser