Random walks can reveal communities/clusters in networks, because they are more likely to stay within a cluster than leave it. Thus, one family of community detection algorithms uses random measure distance between nodes, and then applies clustering methods these distances. Interestingly, information processing the brain may suggest simpler method learning clusters directly from walks. Drawing ...

Graph neural networks (GNNs) have been widely used in graph-structured data computation, showing promising performance various applications such as node classification, link prediction, and network recommendation. Existing works mainly focus on node-wise correlation when doing weighted aggregation of neighboring nodes based attention, dot product by the dense vectors two nodes. This may cause c...

Abstract The objective of clustering is to discover natural groups in datasets and identify geometrical structures which might reside there, without assuming any prior knowledge on the characteristics data. problem can be seen as detecting inherent separations between a given point set metric space governed by similarity function. pairwise similarities all data objects form weighted graph whose...