We proposed a graph structure sequential coding (GSSC) model, including a learnable node-structure feature X, sampler, encoder and classifier.
Aug 1, 2023 · We proposed a graph structure sequential coding (GSSC) model, including a learnable node-structure feature X, sampler, encoder and classifier.
5 days ago · Use decoupled Sampler to sample graphs with different densities.•Use TSMs to capture the semantic information of head node in the sequence.
TL;DR: In this article, a new similarity index based on traditional machine learning, which integrates the concepts of common neighbor, local path, ...
5 days ago · Title: A time sequence coding based node-structure feature model oriented to node classification. · Authors: Ruowang Yu, Yu Xin, Yihong Dong, ...
6 days ago · In this work, we propose new graph features' explanation methods to identify the informative components and important node features. Besides, we ...
As an extension of neural networks, GNN can handle data formats represented by graph structures. In the graph, each node is defined by its own features and its ...
Jan 3, 2024 · The seq2Seq model is a kind of machine learning model that takes sequential data as input and generates also sequential data as output.
Feb 19, 2024 · In this paper, we propose a subgraph encoding based GCN model, SEGCN, with stronger expressive power for social bot detection.
It leverages the differentiable pooling to cluster nodes into fixed groups, and generates a coarse-grained structure accompanied with the shrinking of the ...