We present network embedding algorithms that capture information about a node from the
local distribution over node attributes around it, as observed over random walks following an …

Graph neural networks (GNNs) have achieved tremendous success on multiple graph-
based learning tasks by fusing network structure and node features. Modern GNN models …

Self-supervised learning of graph neural networks (GNN) is in great need because of the
widespread label scarcity issue in real-world graph/network data. Graph contrastive learning …

Graph neural networks (GNNs) have achieved superior performance in various applications,
but training dedicated GNNs can be costly for large-scale graphs. Some recent work started …

Graph is a universe data structure that is widely used to organize data in real-world. Various
real-word networks like the transportation network, social and academic network can be …

Cryptocurrencies gain trust in users by publicly disclosing the full creation and transaction
history. In return, the transaction history faithfully records the whole spectrum of …

Recently, Network Embedding (NE) has become one of the most attractive research topics in
machine learning and data mining. NE approaches have achieved promising performance …

Recently there has been a surge of interest in designing graph embedding methods. Few, if
any, can scale to a large-sized graph with millions of nodes due to both computational …

Proximity preserving and structural role-based node embeddings have become a prime
workhorse of applied graph mining. Novel node embedding techniques are often tested on a …

Network science is the field dedicated to the investigation and analysis of complex systems
via their representations as networks. We normally model such networks as graphs: sets of …