Traditional supervised data classification considers only physical features (eg, distance or
similarity) of the input data. Here, this type of learning is called low level classification. On …

In social networks, by removing some target-sensitive links, privacy protection might be
achieved. However, some hidden links can still be re-observed by link prediction methods …

The goal of semi-supervised learning (SSL) methods is to reduce the amount of labeled
training data required by learning from both labeled and unlabeled instances. Macskassy …

Semi-supervised learning on graphs is a widely applicable problem in network science and
machine learning. Two standard algorithms---label propagation and graph neural networks …

With the recent advances in information networks, the problem of identifying group structure
or communities has received a significant amount of attention. Most of the existing principles …

Purpose–What makes investors tick? Largely counter-intuitive compared to the findings of
most past research, this study explores the possibility that funding investors invest in …

Relational data representations have become an increasingly important topic due to the
recent proliferation of network datasets (eg, social, biological, information networks) and a …

Personalize PageRank (PPR) is an effective relevance (proximity) measure in graph mining.
The goal of this paper is to efficiently compute single node relevance and top-k/highly …

Many collective classification (CC) algorithms have been shown to increase accuracy when
instances are interrelated. However, CC algorithms must be carefully applied because their …

Most real-world information networks, such as social networks, are heterogeneous and as
such, relationships in these networks can be of different types and hence carry differing …