The goal of network representation learning is to learn low-dimensional node embeddings
that capture the graph structure and are useful for solving downstream tasks. However …

Controlling the dissemination of an entity (eg, meme, virus, etc) on a large graph is an
interesting problem in many disciplines. Examples include epidemiology, computer security …

Recently the topic of how to effectively offload cellular traffic onto device-to-device sharing
among users in proximity has been gaining more and more attention from global …

There has been an increased interest in discovering heuristics for combinatorial problems
on graphs through machine learning. While existing techniques have primarily focused on …

Recent studies have shown that graph convolution networks (GCNs) are vulnerable to
carefully designed attacks, which aim to cause misclassification of a specific node on the …

The steady growth of graph data from social networks has resulted in wide-spread research
in finding solutions to the influence maximization problem. In this paper, we propose a …

There has been an increased interest in discovering heuristics for combinatorial problems
on graphs through machine learning. While existing techniques have primarily focused on …

Popularity prediction, with both technological and economic importance, has been
extensively studied for conventional video sharing sites (VSSes), where the videos are …

There has been much effort on studying how social media sites, such as Twitter, help
propagate information in different situations, including spreading alerts and SOS messages …

Social network is becoming an increasingly popular media for information sharing. More and
more people are interacting with others via major social network sites such as Twitter and …