KDD

While most research in online social networks (OSNs) in the past has focused on static friendship networks, social network activity graphs are quite important as well. However, characterizing social network activity graphs is computationally intensive; ...

The effect of the network structure on the dynamics of social and communication networks has been of interest in recent years. It has been observed that network properties such as neighborhood overlap, clustering coefficient, etc. influence the tie ...

Graph databases have emerged as an alternative data model with applications in many complex domains. Typically, the problems to be solved in such domains involve managing and mining huge graphs. The need for efficient processing in such applications has ...

Several key applications like recommender systems require to compute similarities between the nodes (objects or entities) of a bipartite network. These similarities serve many important purposes, such as finding users sharing common interests or items ...

Modularity clustering is a recently introduced clustering objective function for graph clustering. It has been widely used in bioinformatics and social networks. Its relation to data mining field has yet to be explored. In this paper, we show that a ...

An important step in data analysis is the exploration of data. For traditional relational databases one of the most powerful tools for performing such analysis is the relational database and the aggregates and rankings that they can compute: for ...

Although much of the recent work in statistical relational learning has focused on homogeneous networks, many relational domains naturally consist of multiple observed networks, where each network source records a different type of relationship between ...

This paper presents a Bayesian approach to block modelling weighted networks to identify role assignments. This data arises commonly in many forms of social networks where we have a count of the number of communications between users. By using ...

Models for large, sparse graphs are found in many applications and are an active topic in machine learning research. We develop a new generative model that combines rich block structure and simple, efficient estimation by collapsed Gibbs sampling. Novel ...

Centrality is an important notion in network analysis and is used to measure the degree to which network structure contributes to the importance of a node in a network. While many different centrality measures exist, most of them apply to static ...

Graphs are analyzed in many important contexts, including ranking search results based on the hyperlink structure of the world wide web, module detection of proteinprotein interaction networks, and privacy analysis of social networks. Many graphs of ...

Two major types of relational information can be utilized in automatic document classification as background information: relations between terms, such as ontologies, and relations between documents, such as web links or citations in articles. We ...

Graphs are central representations of information in many domains including biological and social networks. Graph visualization is needed for discovering underlying structures or patterns within the data, for example communities in a social network, or ...

Music summarization aims at finding the most representative parts of a music piece (motifs) that can be exploited for efficient music indexing. Here we present a novel approach for motif discovery in music pieces based on an graph spectral ranking. ...

In many scientific and commercial domains, graph as a data structure has become increasingly important for modeling of sophisticated structures. In the past few years, there has been sharp increase in research on mining graph data. We had proposed a ...

In this paper we define the Structural Correlation Pattern (SCP) mining problem, which consists of determining correlations among vertex attributes and dense components in an undirected graph. Vertex attributes play an important role in several real-...

The understanding of dynamics of data streams is greatly affected by the choice of temporal resolution at which the data are discretized, aggregated, and analyzed. Our paper focuses explicitly on data streams represented as dynamic networks. We propose ...

As the rapid development of all kinds of online databases, huge heterogeneous information networks thus derived are ubiquitous. Detecting evolutionary communities in these networks can help people better understand the structural evolution of the ...

The increasing availability of participatory web and social media presents enormous opportunities to study human relations and collective behaviors. Many applications involving decision making want to obtain certain generalized properties about the ...

In many application domains, graphs are utilized to model entities and their relationships, and graph mining is important to detect patterns within these relationships. While the majority of recent data mining techniques deal with static graphs that do ...

In many applications the available data give rise to an attributed graph, with the nodes corresponding to the entities of interest, edges to their relationships and attributes on both provide additional characteristics. To mine such data structures we ...

Frequent subgraph mining has always been an important issue in data mining. Several frequent graph mining methods have been developed for mining graph transactions. However, these methods become less usable when the dataset is a single large graph. Also,...