arXiv:2605. 26290v2 Announce Type: replace Abstract: Temporal signed networks (TSNs) model the time evolution of cooperative and adversarial relationships that arise in applications such as social media analysis, trust and reputation systems, and financial transaction networks.
By Derek Regier, Andrew Polyak, Aresh Dadlani, Khosro Salmani
arXiv:2607. 07716v1 Announce Type: cross Abstract: Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy.
By Yazheng Liu, Xi Zhang, Sihong Xie, Hui Xiong
arXiv:2606. 01176v1 Announce Type: new Abstract: Real temporal interaction streams carry predictive structure in short-horizon motif patterns -- repetition, reciprocity, star diversity, triadic flow -- that vanilla temporal graph neural networks (TGNNs) often fail to expose to their edge scorers.
By Dylan Sandfelder, Mihai Cucuringu, Xiaowen Dong
arXiv:2607. 27350v1 Announce Type: new Abstract: Sybil bots are Ethereum actors that imitate legitimate users to extract airdrop rewards or influence governance.
By Micha{\l} Bartnicki, Jaros{\l}aw A. Chudziak
arXiv:2311.16139v3 Announce Type: replace-cross
Abstract: Graph Neural Networks (GNNs) have become indispensable tools for learning from graph structured data, catering to various applications such a...
By Zeyu Song, Ehsanul Kabir, Shagufta Mehnaz
The paper introduces an asynchronous message‑passing framework for Graph Neural Networks to mitigate oversquashing, a problem where distant nodes cannot effectively communicate due to structural bottlenecks. Unlike conventional synchronous updates, the method updates a centrality‑guided batch of nodes at each layer, allowing information to propagate sequentially and reducing the need for increased channel capacity. Experiments on six standard and two long‑range graph classification benchmarks show notable performance gains, including 5 % improvement on REDDIT‑BINARY and 4 % on Peptides‑struct.
By Kushal Bose, Swagatam Das