Biao Xiang

Context-aware ranking in web search

The context of a search query often provides a search engine meaningful hints for answering the current query better. Previous studies on context-aware search were either focused on the development of context models or limited to a relatively small scale investigation under a controlled laboratory setting. Particularly, about context-aware ranking for Web search, the following two critical problems are largely remained unsolved. First, how can we take advantage of different types of contexts in ranking? Second, how can we integrate context information into a ranking model? In this paper, we tackle the above two essential problems analytically and empirically. We develop different ranking principles for different types of contexts. Moreover, we adopt a learning-to-rank approach and integrate the ranking principles into a state-of-the-art ranking model by encoding the context information as features of the model. We empirically test our approach using a large search log data set obtained from a major commercial search engine. Our evaluation uses both human judgments and implicit user click data. The experimental results clearly show that our context-aware ranking approach improves the ranking of a commercial search engine which ignores context information. Furthermore, our method outperforms a baseline method which considers context information in ranking.

Influence Maximization over Large-Scale Social Networks: A Bounded Linear Approach

Information diffusion in social networks is emerging as a promising solution to successful viral marketing, which relies on the effective and efficient identification of a set of nodes with the maximal social influence. While there are tremendous efforts on the development of social influence models and algorithms for social influence maximization, limited progress has been made in terms of designing both efficient and effective algorithms for finding a set of nodes with the maximal social influence. To this end, in this paper, we provide a bounded linear approach for influence computation and influence maximization. Specifically, we first adopt a linear and tractable approach to describe the influence propagation. Then, we develop a quantitative metric, named Group-PageRank, to quickly estimate the upper bound of the social influence based on this linear approach. More importantly, we provide two algorithms Linear and Bound, which exploit the linear approach and Group-PageRank for social influence maximization. Finally, extensive experimental results demonstrate that (a) the adopted linear approach has a close relationship with traditional models and Group-PageRank provides a good estimation of social influence; (b) Linear and Bound can quickly find a set of the most influential nodes and both of them are scalable for large-scale social networks.

On approximation of real-world influence spread

To find the most influential nodes for viral marketing, several models have been proposed to describe the influence propagation process. Among them, the Independent Cascade (IC) Model is most widely-studied. However, under IC model, computing influence spread (i.e., the expected number of nodes that will be influenced) for each given seed set has been proved to be #P-hard. To that end, in this paper, we propose GS algorithm for quick approximation of influence spread by solving a linear system, based on the fact that propagation probabilities in real-world social networks are usually quite small. Furthermore, for better approximation, we study the structural defect problem existing in networks, and correspondingly, propose enhanced algorithms, GSbyStep and SSSbyStep, by incorporating the Maximum Influence Path heuristic. Our algorithms are evaluated by extensive experiments on four social networks. Experimental results show that our algorithms can get better approximations to the IC model than the state-of-the-arts.

An Influence Propagation View of PageRank

For a long time, PageRank has been widely used for authority computation and has been adopted as a solid baseline for evaluating social influence related applications. However, when measuring the authority of network nodes, the traditional PageRank method does not take the nodes’ prior knowledge into consideration. Also, the connection between PageRank and social influence modeling methods is not clearly established. To that end, this article provides a focused study on understanding PageRank as well as the relationship between PageRank and social influence analysis. Along this line, we first propose a linear social influence model and reveal that this model generalizes the PageRank-based authority computation by introducing some constraints. Then, we show that the authority computation by PageRank can be enhanced if exploiting more reasonable constraints (e.g., from prior knowledge). Next, to deal with the computational challenge of linear model with general constraints, we provide an upper bound for identifying nodes with top authorities. Moreover, we extend the proposed linear model for better measuring the authority of the given node sets, and we also demonstrate the way to quickly identify the top authoritative node sets. Finally, extensive experimental evaluations on four real-world networks validate the effectiveness of the proposed linear model with respect to different constraint settings. The results show that the methods with more reasonable constraints can lead to better ranking and recommendation performance. Meanwhile, the upper bounds formed by PageRank values could be used to quickly locate the nodes and node sets with the highest authorities.

Individual Influence Maximization via Link Recommendation

Recent years have witnessed the increasing interest in exploiting social influence in social networks for many applications, such as viral marketing. Most of the existing research focused on identifying a subset of influential individuals with the maximum influence spread. However, in the real-world scenarios, many individuals also care about the influence of herself and want to improve it. In this paper, we consider such a problem that maximizing a target individual’s influence by recommending new links. Specifically, if a given individual/node makes new links with our recommended nodes then she will get the maximum influence gain. Along this line, we formulate this link recommendation problem as an optimization problem and propose the corresponding objective function. As it is intractable to obtain the optimal solution, we propose greedy algorithms with a performance guarantee by exploiting the submodular property. Furthermore, we study the optimization problem under a specific influence propagation model (i.e., Linear model) and propose a much faster algorithm (uBound), which can handle large scale networks without sacrificing accuracy. Finally, the experimental results validate the effectiveness and efficiency of our proposed algorithms.