Graph neural networks (GNNs) have emerged as effective approaches for graph analysis, especially in the scenario of semi-supervised learning. Despite its success, GNN often suffers from over-smoothing and over-fitting problems, which affects performance on node classification tasks. We analyze that an alternative method, label propagation algorithm (LPA), avoids aforementioned problems thus it ...

Label Propagation Algorithm (LPA) and Graph Convolutional Neural Networks (GCN) are both message passing algorithms on graphs. Both solve the task of node classification, but LPA propagates label information across edges graph, while GCN transforms feature information. However, conceptually similar, theoretical relationship between has not yet been systematically investigated. Moreover, it is u...

We consider streaming prediction model for tree Markov Random fields. Given the random field, at any point in time we may perform one of three actions: i) predict a label at a vertex on the tree ii) update by associating a label with a vertex or iii) delete the label at a vertex. Using the standard methodology of belief propagation each such action requires time linear in the size of the tree. ...

Label propagation has proven to be an extremely fast method for detecting communities in large complex networks. Furthermore, due to its simplicity, it is also currently one of the most commonly adopted algorithms in the literature. Despite various subsequent advances, an important issue of the algorithm has not yet been properly addressed. Random (node) update orders within the algorithm sever...

Recent studies have shown that graph-based approaches are effective for semi-supervised learning. The key idea behind many graph-based approaches is to enforce the consistency between the class assignment of unlabeled examples and the pairwise similarity between examples. One major limitation with most graph-based approaches is that they are unable to explore dissimilarity or negative similarit...