3 accepted papers
Semi-supervised graph anomaly detection (GAD), which assumes a subset of annotated normal nodes available during training, is among the most widely explored applications. However, the normality learned by existing semi-supervised GAD methods is limited to the labeled normal nodes, often inclining to…
Activities in numerous evolving systems can be represented as dynamic graphs in snapshot form at different time intervals, i.e., discrete-time dynamic graphs (DTDGs). Existing methods show impressive advances in capturing historical temporal evolution patterns in DTDGs, but they focus on addressing …
Graph Neural Networks (GNNs) have been successful in modeling homophily graphs, but their performance is limited when applied to heterophily graphs. Some methods based on the LSTM can mitigate this issue while they artificially fix the input sequence which is reversed from the collection order of th…