Information diffusion has been widely studied in networks, aiming to model the spread of information among objects when they are connected with each other. Most of the current research assumes the underlying network is homogeneous, i.e., objects are of the same type and they are connected by links with the same semantic meanings. However, in the real word, objects are connected via different types of relationships, forming multi-relational heterogeneous information networks.

Mining outliers in a heterogeneous information network is a challenging problem: It is even unclear what should be outliers in a large heterogeneous network (e.g., Outliers in the entire bibliographic network consisting of authors, titles, papers and venues). In this study, we propose an interesting class of outliers, query-based sub network outliers: Given a heterogeneous network, a user raises a query to retrieve a set of task-relevant sub networks, among which, sub network outliers are those that significantly deviate from others (e.g., Outliers of author groups among those studying "topic modeling"). We formalize this problem and propose a general framework, where one can query for finding sub network outliers with respect to different semantics. We introduce the notion of sub network similarity that captures the proximity between two sub networks by their membership distributions. We propose an outlier detection algorithm to rank all the sub networks according to their outlierness without tuning parameters. Our quantitative and qualitative experiments on both synthetic and real data sets show that the proposed method outperforms other baselines.

News data is one of the most abundant and familiar data sources. News data can be systematically utilized and ex- plored by database, data mining, NLP and information re- trieval researchers to demonstrate to the general public the power of advanced information technology. In our view, news data contains rich, inter-related and multi-typed data objects, forming one or a set of gigantic, interconnected, het- erogeneous information networks. Much knowledge can be derived and explored with such an information network if we systematically develop effective and scalable data-intensive information network analysis technologies. By further developing a set of information extraction, in- formation network construction, and information network mining methods, we extract types, topical hierarchies and other semantic structures from news data, construct a semi- structured news information network NewsNet. Further, we develop a set of news information network exploration and mining mechanisms that explore news in multi-dimensional space, which include (i) OLAP-based operations on the hierarchical dimensional and topical structures and rich-text, such as cell summary, single dimension analysis, and promo- tion analysis, (ii) a set of network-based operations, such as similarity search and ranking-based clustering, and (iii) a set of hybrid operations or network-OLAP operations, such as entity ranking at different granularity levels. These form the basis of our proposed NewsNetExplorer system. Although some of these functions have been studied in recent research, effective and scalable realization of such functions in large networks still poses multiple challenging research problems. Moreover, some functions are our on-going research tasks. By integrating these functions, NewsNetExplorer not only provides with us insightful recommendations in NewsNet exploration system but also helps us gain insight on how to perform effective information extraction, integration and mining in large unstructured datasets.

A database contains rich, inter-related, multi-typed data and information, forming one or a set of gigantic, intercon- nected, heterogeneous information networks. Much knowl- edge can be derived from such information networks if we systematically develop an effective and scalable database-oriented information network analysis technology. In this system demo, we take a computer science research publica- tion network as an example, which is an information net- work derived from an integration of DBLP, other web-based information about researchers, and partially available cita- tion data, and construct a Research-Insight system in order to demonstrate the power of database-oriented information network analysis. We show that nontrivial research insight can be obtained from such analysis, including (1) ranking, clustering, classification and similarity search of researchers, terms and venues for research subfields and themes, (2) recommending good researchers and good research papers to read or cite when conducting research on certain topics (3) predicting potential collaborators for certain theme-oriented research, and (4) predicting advisor-advisee rela- tionships and affiliation history based on historical research publications. Although some of these functions have been studied in recent research, effective and scalable realization of such functions in large networks still poses challenging research problems. Moreover, some function are our on- going research tasks. By integrating these functionalities, Research-Insight may not only provide with us insightful rec- ommendations in CS research but also help us gain insight on how to perform effective data mining in large databases.