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Found 4 papers, 1 papers with code
Semantic Random Walk for Graph Representation Learning in Attributed Graphs
Different from existing embedding methods that treat the incorporation of graph structure and semantic as the simple combination of two optimization objectives, we propose a novel semantic graph representation (SGR) method to formulate the joint optimization of the two heterogeneous sources into a common high-order proximity based framework.
Trading off Quality for Efficiency of Community Detection: An Inductive Method across Graphs
The trained model is then directly generalized to new unseen graphs for online CD without additional optimization, where a better trade-off between quality and efficiency can be achieved.
Trading Quality for Efficiency of Graph Partitioning: An Inductive Method across Graphs
IGP is also a generic framework that can capture the permutation invariant partitioning ground-truth of historical snapshots in the offline training and tackle the online GP on graphs with non-fixed number of nodes and clusters.
GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks
Different from conventional techniques of temporal link prediction that ignore the potential non-linear characteristics and the informative link weights in the dynamic network, we introduce a novel non-linear model GCN-GAN to tackle the challenging temporal link prediction task of weighted dynamic networks.
