Predicting and Identifying behavioral analysis in social media using big data analytics is
exceptionally repetitive. Since data in motion are difficult to capture and process with …

Analyzing “what topics” a user discusses with others is important in social network analysis.
Since social relationships can be represented as multiobject relationships (eg, those …

Affinity Propagation is a clustering algorithm used in many applications. It iteratively updates
messages between data points until convergence. The message updating process enables …

The ability to predict the activities of users is an important one for recommender systems and
analyses of social media. User activities can be represented in terms of relationships …

Network representation learning is a de facto tool for graph analytics. The mainstream of the
previous approaches is to factorize the proximity matrix between nodes. However, if n is the …

Locally Linear Embedding (LLE) is a popular approach to dimensionality reduction as it can
effectively represent nonlinear structures of high-dimensional data. For dimensionality …

Random forest is an ensemble approach based on decision trees. It computes the best split
in each node in terms of impurity reduction. However, the impurity computations incur high …

The b-matching graph is a useful approach to computing a graph from high-dimensional
data. Unlike the k-NN graph that greedily connects each data point to its k nearest neighbors …

The semantics distributed over large-scale knowledge bases can be used to intermediate
heterogeneous users' activity logs created in services; such information can be used to …

One-class SVM is a popular method for one-class classification but it needs high
computation cost. This paper proposes Quix as an efficient training algorithm for one-class …