graph embedding

Familial Clustering For Weakly-labeled Android Malware Using Hybrid Representation Learning

Labeling malware or malware clustering is important for identifying new security threats, triaging and building reference datasets. The state-of-the-art Android malware clustering approaches rely heavily on the raw labels from commercial AntiVirus …

Adaptive knowledge subgraph ensemble for robust and trustworthy knowledge graph completion

Knowledge graph (KG) embedding approaches are widely used to infer underlying missing facts based on intrinsic structure information. However, the presence of noisy facts in automatically extracted or crowdsourcing KGs significantly reduces the …

Learning Graph Embedding With Adversarial Training Methods

Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph-analytics tasks like link prediction and graph clustering. Most approaches on graph embedding focus on preserving the graph structure or minimizing the …