Many real-world social networks exist in the form of a complex network, which includes very
large scale networks with structured or unstructured data and a set of graphs. This complex …

Data is flourishing day by day to a large extent, the data that need to be analyzed are not just
large, but it may be high dimensional, heterogeneous, complex, unstructured, incomplete …

Network embedding has been widely used in social recommendation and network analysis,
such as recommendation systems and anomaly detection with graphs. However, most of …

Finding connected components is a fundamental task in applications dealing with graph
analytics, such as social network analysis, web graph mining and image processing. The …

Many of today's large datasets are organized as a graph. Due to their size it is often
infeasible to process these graphs using a single machine. Therefore, many software …

In the information age, sentiment classification of Internet topics is of great significance. This
paper proposes a microblog sentiment classification approach with parallel support vector …

Money launderers exploit the weaknesses in detection systems by purposefully placing their
ill-gotten money into multiple accounts, at different banks. That money is then layered and …

The problem of finding connected components in a graph is common to several applications
dealing with graph analytics, such as social network analysis, web graph mining and image …

The computation of nodes centrality is of great importance for the analysis of graphs. The
current flow betweenness is an interesting centrality index that is computed by considering …

Graph OLAP is a technology that generates aggregates or summaries of a large-scale graph
based on the properties (or dimensions) associated with its nodes and edges, and in turn …