With the development of Web applications, textual documents are not only getting richer, but also ubiquitously interconnected with users and other objects in various ways, which brings about text-rich heterogeneous information networks. Topic models have been proposed and shown to be useful for document analysis, and the interactions among multi-typed objects play a key role at disclosing the rich semantics of the network. However, most of topic models only consider the textual information while ignore the network structures or can merely integrate with homogeneous networks. None of them can handle heterogeneous information network well. In this paper, we propose a novel topic model with biased propagation (TMBP) algorithm to directly incorporate heterogeneous information network with topic modeling in a unified way. The underlying intuition is that multi-typed objects should be treated differently along with their inherent textual information and the rich semantics of the heterogeneous information network. A simple and unbiased topic propagation across such a heterogeneous network does not make much sense. Consequently, we investigate and develop two biased propagation frameworks, the biased random walk framework and the biased regularization framework, for the TMBP algorithm from different perspectives, which can discover latent topics and identify clusters of multi-typed objects simultaneously. We extensively evaluate the proposed approach and compare to the state-of-the-art techniques on several datasets. Experimental results demonstrate that the improvement in our proposed approach is consistent and promising.

With the maturity and wide availability of GPS, wireless, telecommunication, and Web technologies, massive amounts of object movement data have been collected from various moving object targets, such as animals, mobile devices, vehicles, and climate radars. Analyzing such data has deep implications in many applications, such as, ecological study, traffic control, mobile communication management, and climatological forecast. In this article, we focus our study on animal movement data analysis and examine advanced data mining methods for discovery of various animal movement patterns. In particular, we introduce a moving object data mining system, MoveMine, which integrates multiple data mining functions, including sophisticated pattern mining and trajectory analysis. In this system, two interesting moving object pattern mining functions are newly developed: (1) periodic behavior mining and (2) swarm pattern mining . For mining periodic behaviors, a reference location-based method is developed, which first detects the reference locations, discovers the periods in complex movements, and then finds periodic patterns by hierarchical clustering. For mining swarm patterns, an efficient method is developed to uncover flexible moving object clusters by relaxing the popularly-enforced collective movement constraints. In the MoveMine system, a set of commonly used moving object mining functions are built and a user-friendly interface is provided to facilitate interactive exploration of moving object data mining and flexible tuning of the mining constraints and parameters. MoveMine has been tested on multiple kinds of real datasets, especially for MoveBank applications and other moving object data analysis. The system will benefit scientists and other users to carry out versatile analysis tasks to analyze object movement regularities and anomalies. Moreover, it will benefit researchers to realize the importance and limitations of current techniques and promote future studies on moving object data mining. As expected, a mastery of animal movement patterns and trends will improve our understanding of the interactions between and the changes of the animal world and the ecosystem and therefore help ensure the sustainability of our ecosystem.

Information networks are ubiquitous in many applications and analysis on such networks has attracted significant attention in the academic communities. One of the most important aspects of information network analysis is to measure similarity between nodes in a network. SimRank is a simple and influential measure of this kind, based on a solid theoretical "random surfer" model. Existing work computes SimRank similarity scores in an iterative mode. We argue that the iterative method can be infeasible and inefficient when, as in many real-world scenarios, the networks change dynamically and frequently. We envision non-iterative method to bridge the gap. It allows users not only to update the similarity scores incrementally, but also to derive similarity scores for an arbitrary subset of nodes. To enable the non-iterative computation, we propose to rewrite the SimRank equation into a non-iterative form by using the Kronecker product and vectorization operators. Based on this, we develop a family of novel approximate SimRank computation algorithms for static and dynamic information networks, and give their corresponding theoretical justification and analysis. The non-iterative method supports efficient processing of various node analysis including similarity tracking and centrality tracking on evolving information networks. The effectiveness and efficiency of our proposed methods are evaluated on synthetic and real data sets.

Information network contains abundant knowledge about relationships among people or entities. Unfortunately, such kind of knowledge is often hidden in a network where different kinds of relationships are not explicitly categorized. For example, in a research publication network, the advisor-advisee relationships among researchers are hidden in the coauthor network. Discovery of those relationships can benefit many interesting applications such as expert finding and research community analysis. In this paper, we take a computer science bibliographic network as an example, to analyze the roles of authors and to discover the likely advisor-advisee relationships. In particular, we propose a time-constrained probabilistic factor graph model (TPFG), which takes a research publication network as input and models the advisor-advisee relationship mining problem using a jointly likelihood objective function. We further design an efficient learning algorithm to optimize the objective function. Based on that our model suggests and ranks probable advisors for every author. Experimental results show that the proposed approach infer advisor-advisee relationships efficiently and achieves a state-of-the-art accuracy (80-90%). We also apply the discovered advisor-advisee relationships to bole search, a specific expert finding task and empirical study shows that the search performance can be effectively improved (+4.09% by [email protected]).

With the maturity of GPS, wireless, and Web technologies, increasing amounts of movement data collected from various moving objects, such as animals, vehicles, mobile devices, and climate radars, have become widely available. Analyzing such data has broad applications, e.g., in ecological study, vehicle control, mobile communication management, and climatological forecast. However, few data mining tools are available for flexible and scalable analysis of massive-scale moving object data. Our system, MoveMine, is designed for sophisticated moving object data mining by integrating several attractive functions including moving object pattern mining and trajectory mining. We explore the state-of-the-art and novel techniques at implementation of the selected functions. A user-friendly interface is provided to facilitate interactive exploration of mining results and flexible tuning of the underlying methods. Since MoveMine is tested on multiple kinds of real data sets, it will benefit users to carry out versatile analysis on these kinds of data. At the same time, it will benefit researchers to realize the importance and limitations of current techniques as well as the potential future studies in moving object data mining.

Document networks, i.e., networks associated with text information, are becoming increasingly popular due to the ubiquity of Web documents, blogs, and various kinds of online data. In this paper, we propose a novel topic modeling framework for document networks, which builds a unified generative topic model that is able to consider both text and structure information for documents. A graphical model is proposed to describe the generative model. On the top layer of this graphical model, we define a novel multivariate Markov random field for topic distribution random variables for each document, to model the dependency relationships among documents over the network structure. On the bottom layer, we follow the traditional topic model to model the generation of text for each document. A joint distribution function for both the text and structure of the documents is thus provided. A solution to estimate this topic model is given, by maximizing the log-likelihood of the joint probability. Some important practical issues in real applications are also discussed, including how to decide the topic number and how to choose a good network structure. We apply the model on two real datasets, DBLP and Cora, and the experiments show that this model is more effective in comparison with the state-of-the-art topic modeling algorithms.

A heterogeneous information network is an information network

Nowadays, most business, administration, and/or scientific databases contain both structured attributes and text attributes. We call a database that consists of both multidimensional structured data and narrative text data as multidimensional text database . Searching, OLAP, and mining such databases pose many research challenges. To enhance the power of data analysis, interesting entities and relationships can be extracted from such databases to derive heterogeneous information networks, which in turn will substantially increase the power and flexibility of data exploration in such databases. Based on our previous studies on TextCube [1], TopicCube [2], and information network analysis, such as RankClus [3] and NetClus [4], we construct iNextCube , an i nformation- N etwork- e nhanced te xt Cube . In this demo, we show the power of iNextCube in the search and analysis of two multidimensional text databases: (i) a DBLP-based CS bibliographic database, and (ii) an online news database.