Chengli Zhao

Identifying missing and spurious interactions in directed networks

Recent years, the studies of link prediction have been overwhelmingly emphasizing on undirected networks. Compared with it, how to identify missing and spurious interactions in directed networks has received less attention and still is not well understood. In this paper, we make use of classical link prediction indices for undirected networks, adapt them to directed version which could predict both the existence and direction of an arc between two nodes, and investigate their prediction ability on six real-world directed networks. Experimental results demonstrate that those modified indices perform quite well in directed networks. Compared with bifan predictor, some of them can provide more accurate predictions.

Identifying Missing and Spurious Interactions in Directed Networks

Recent years, the studies of link prediction have been overwhelmingly emphasizing on undirected networks. Compared with it, how to identify missing and spurious interactions in directed networks has received less attention and still is not well understood. In this paper, we make use of classical link prediction indices for undirected networks, adapt them to directed version which could predict both the existence and direction of an arc between two nodes, and investigate their prediction ability on six real-world directed networks. Experimental results demonstrate that those modified indices perform quite well in directed networks. Compared with Bi-fan predictor, some of them can provide more accurate predictions.

An Event Graph Model for Discovering Trends from Text Streams

In this paper, we formally define and study the event graph model based on set theory and multi-relations theory, and discuss the methods of modeling event and event relations in detail. The event graph model is mainly designed to extract the potential events and the relationships between events from massive text streams, and further discover the trends embodied in the contents in text streams. We also study the connectivity of the event graph model, and give the equivalent conditions to determine the connectivity of event graph.

A General Framework: Combining Statistical Models and Network Structure for Deep Content Mining

In this paper, we propose a general framework for content mining, which combines statistical model and network structure to leverages the power of both statistical topic models and network method. This method is a novel view to both text oriented method and network oriented method. The proposed framework is general, it can be applied to any text collections with an associated network structure.