Mengting Hu, Shiwan Zhao, Li Zhang, Keke Cai, Zhong Su, Renhong Cheng, Xiaowei Shen. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

The first ACM international workshop on big network analytics is held in Indianapolis, Indiana, USA on October 24, 2016 and co-located with the ACM 25th Conference on Information and Knowledge Management (CIKM). The main objective of the workshop is to provide a forum for presenting the most recent advances in mining big networks to unearth rich knowledge. It is related to information retrieval, Web mining, social network analysis, and computational advertising. The anticipated outcome includes a fruitful discussion about the emerging challenges in this field, the development of novel theories for mining big networks, and motivating the interesting applications. The broader anticipated outcome includes: fostering future research directions, publishing high quality papers, attracting new researchers to this field, and concrete solutions to the existing problems.

Finding a subset of users to statistically represent the original social network is a fundamental issue in Social Network Analysis (SNA). The problem has not been extensively studied in existing literature. In this paper, we present a formal definition of the problem of \textbf{sampling representative users} from social network. We propose two sampling models and theoretically prove their NP-hardness. To efficiently solve the two models, we present an efficient algorithm with provable approximation guarantees. Experimental results on two datasets show that the proposed models for sampling representative users significantly outperform (+6\%-23\% in terms of Precision@100) several alternative methods using authority or structure information only. The proposed algorithms are also effective in terms of time complexity. Only a few seconds are needed to sampling ~300 representative users from a network of 100,000 users.All data and codes are publicly available.

Patents are of crucial importance for businesses, because they provide legal protection for the invented techniques, processes or products. A patent can be held for up to 20 years. However, large maintenance fees need to be paid to keep it enforceable. If the patent is deemed not valuable, the owner may decide to abandon it by stopping paying the maintenance fees to reduce the cost. For large companies or organizations, making such decisions is difficult because too many patents need to be investigated. In this paper, we introduce the new patent mining problem of automatic patent maintenance prediction, and propose a systematic solution to analyze patents for recommending patent maintenance decision. We model the patents as a heterogeneous time-evolving information network and propose new patent features to build model for a ranked prediction on whether to maintain or abandon a patent. In addition, a network-based refinement approach is proposed to further improve the performance. We have conducted experiments on the large scale United States Patent and Trademark Office (USPTO) database which contains over four million granted patents. The results show that our technique can achieve high performance.

We study a novel problem of social context summarization for Web documents. Traditional summarization research has focused on extracting informative sentences from standard documents. With the rapid growth of online social networks, abundant user generated content (e.g., comments) associated with the standard documents is available. Which parts in a document are social users really caring about? How can we generate summaries for standard documents by considering both the informativeness of sentences and interests of social users? This paper explores such an approach by modeling Web documents and social contexts into a unified framework. We propose a dual wing factor graph (DWFG) model, which utilizes the mutual reinforcement between Web documents and their associated social contexts to generate summaries. An efficient algorithm is designed to learn the proposed factor graph model.Experimental results on a Twitter data set validate the effectiveness of the proposed model. By leveraging the social context information, our approach obtains significant improvement (averagely +5.0%-17.3%) over several alternative methods (CRF, SVM, LR, PR, and DocLead) on the performance of summarization.

Social influence is a complex and subtle force that governs the dynamics of social networks. In the past years, a lot of research work has been conducted to understand the spread patterns of social influence. However, most of approaches assume that influence exists between users with active social interactions, but ignore the question of what kind of influence happens between them. As such one interesting and also fundamental question is raised here: "in a social network, could the social connection reflect users'influence from both positive and negative aspects?". To this end, an Opinion Oriented Link Analysis Model (OOLAM) is proposed in this paper to characterize users' influence personae in order to exhibit their distinguishing influence ability in the social network. In particular, three types of influence personae are generalized and the problem of influence persona discovery is formally defined. Within the OOLAM model, two factors, i.e., opinion consistency and opinion creditability, are defined to capture the persona information from public opinion perspective. Extensive experimental studies have been performed to demonstrate the effectiveness of the proposed approach on influence persona analysis using real web data sets.

Retweeting is an important action (behavior) on Twitter, indicating the behavior that users re-post microblogs of their friends. While much work has been conducted for mining textual content that users generate or analyzing the social network structure, few publications systematically study the underlying mechanism of the retweeting behaviors. In this paper, we perform an interesting analysis for the problem on Twitter. We have found that almost 25.5% of the tweets posted by users are actually retweeted from friends' blog spaces. Our investigation unveils that for the retweet behaviors, some statistics still follows the power law distribution, while some others violate the state-of-the-art distribution for Web. Based on these important observations, we propose a factor graph model to predict users' retweeting behaviors. Experimental results on the Twitter data set show that our method can achieve a precision of 28.81% and recall of 37.33% for prediction of the retweet behaviors.

The emergence of new social media such as blogs, message boards, news, and web content in general has dramatically changed the ecosystems of corporations. Consumers, non-profit organizations, and other forms of communities are extremely vocal about t

It is well known that Web users create links with different intentions. However, a key question, which is not well studied, is how to categorize the links and how to quantify the strength of the influence of a Web page on another if there is a link between the two linked Web pages. In this paper, we focus on the problem of link semantics analysis, and propose a novel supervised learning approach to build a model, based on a training link-labeled and link-weighted graph where a link-label represents the category of a link and a link-weight represents the influence of one web page on the other in a link. Based on the model built, we categorize links and quantify the influence of Web pages on the others in a large graph in the same application domain. We discuss our proposed approach, namely pairwise restricted Boltzmann machines (PRBMs), and conduct extensive experimental studies to demonstrate the effectiveness of our approach using large real datasets.

The emergence of new social media such as blogs, message boards, news, and Web content in general has dramatically changed the ecosystems of corporations. Consumers, non-profit organizations, and other forms of communities are extremely vocal about their opinions and perceptions on companies and their brands on the Web. The ability to leverage such "voice of the Web" to gain consumer, brand, and market insights can be truly differentiating and valuable to todaypsilas corporations. In particular, one important form of insights can be derived from sentiment analysis on Web content. Sentiment analysis traditionally emphasizes on classification of Web comments into positive, neutral, and negative categories. This paper goes beyond sentiment classification by focusing on techniques that could detect the topics that are highly correlated with the positive and negative opinions. Such techniques, when coupled with sentiment classification, can help the business analysts to understand both the overall sentiment scope as well as the drivers behind the sentiment. In this paper, we describe our overall sentiment analysis system that consists of such sentiment analysis techniques. We then detail a novel topic detection method using point-wise mutual information and term frequency distribution. We demonstrate the effectiveness of our overall approaches via several case studies on different social media data sets.

This poster focuses on the study of term context dependence in the application of sentence retrieval. Based on Markov Random Field (MRF), three forms of dependence among query terms are considered. Under different assumptions of term dependence relationship, three feature functions are defined, with the purpose to utilize association features between query terms in sentence to evaluate the relevance of sentence. Experimental results have proven the efficiency of the proposed retrieval models in improving the performance of sentence retrieval.

This paper makes an intensive investigation of the application of Bayesian network in sentence retrieval and introduces three Bayesian network based sentence retrieval models with or without consideration of term relationships. Term relationships in this paper are considered from two perspectives: relationships between pairs of terms and relationships between terms and term sets. Experiments have proven the efficiency of Bayesian network in the application of sentence retrieval. Particularly, retrieval result with consideration of the second kind of term relationship performs better in improving retrieval precision.

A number of existing information retrieval systems propose the notion of query context to combine the knowledge of query and user into retrieval to reveal the most exact description of user's information needs. In this paper we interpret query context as a document consisting of sentences related to the current query. This kind of query context is used to re-estimate the relevance probabilities of top-ranked documents and then re-rank top-ranked documents. The experiments show that the proposed context-based approach for information retrieval can greatly improved relevance of search results.

Data multicast technology has received much attention and many achievements have been attained for it. However, most existing multicast techniques rely heavily on client input for data dissemination. In this paper, we investigated an efficient way of automatically identifying the changes of clients' preferences. Our scheme utilizes clustering technique to realize the group of similar clients, and furthermore, dynamically adjusts these clusters to cope with the changes of clients' demands. The rationale underneath is that clients within the same cluster possess similar preferences. The goal of our method is to dynamically present information tailored to specific groups of clients and ensure that the multicast information clients received can satisfy their interests. To conclude this paper, we experimented the recommended method on three different modes of clients' preferences. The simulation results prove it an effective scheme to reduce the amount of superfluous data clients received for certain preference distributions.