Given a directed graph of millions of nodes, how can we automatically spot anomalous,
suspicious nodes, judging only from their connectivity patterns? Suspicious graph patterns …

A key operation in network analysis is the discovery of cohesive subgraphs. The notion of k-
truss has gained considerable popularity in this regard, based on its rich structure and …

Data collected nowadays by social-networking applications create fascinating opportunities
for building novel services, as well as expanding our understanding about social structures …

The exponential number of possible subgraphs makes the problem of frequent subgraph
mining a challenge. The set of maximal frequent subgraphs is much smaller to that of the set …

Due to the inaccuracy, incompleteness and noise in data from real applications, uncertainty
is a natural feature of real-world networks. In such networks, each edge is associated with a …

Uncertain data are data with uncertainty information, which exist widely in database
applications. In recent years, uncertainty in data has brought challenges in almost all …

Given a directed graph of millions of nodes, how can we automatically spot anomalous,
suspicious nodes judging only from their connectivity patterns? Suspicious graph patterns …

During the last decade, cloud computing became a natural choice to host and provide
various computing resources as on-demand services. The correct characterization and …

Existing studies on graph mining focus on exact graphs that are precise and complete.
However, graph data tends to be uncertain in practice due to noise, incompleteness and …

Frequent subgraph mining has been extensively studied on certain graph data. However,
uncertainties are inherently accompanied with graph data in practice, and there is very few …