TL;DR
DCM-Net is proposed, a novel Dual Conditional Modulation Graph Attention Network that dynamically optimizes both the input features and the learning process of the graph attention network, and achieves the best average performance across ROC-AUC, MAP, and PR-AUC.
Dynamic bipartite graphs (DBGs) are widely used in real-world scenarios, where representation learning is particularly challenging due to the heterogeneity of node types and the temporal evolution of interactions. A key difficulty lies in jointly capturing non-stationary micro-level preference dynamics and macro-level...
A statistical feature augmentation method that explicitly encodes behavioral interaction statistics into the input feature space and consistently improves detection performance is proposed, showcasing a promising approach for merging classical network analysis with deep learning.
The basic ideas of online diffusion are introduced by formalising cascade and global graphs, and the extraction of structural and temporal features is described, and the shift toward modelling stochastic cascade dynamics is highlighted.
Identifying influential nodes is a fundamental task in complex network analysis, with applications in epidemic control, information diffusion, and network robustness. Existing centrality-based and learning-based methods often provide limited integration of structural information across different network scales. To addr...
This paper proposes ATNSF, a temporal graph learning framework with a hybrid negative sampling strategy that uses a portion of historical edges as hard negatives and introduces a pairwise sampled softmax loss that contrasts each positive instance with a batch of negatives to learn more discriminative representations.
With the evolution of modern intelligent networks, accurate traffic matrix (TM) prediction has become critical for proactive network management, but remains challenging due to high-dimensional and non-stationary spatio-temporal dependencies. We propose TraDiff, an image-based conditional diffusion framework for TM pred...
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