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36
Crosslingual knowledge linking across wiki knowledge bases
- In WWW’12, 459–468. ACM
, 2012
"... Wikipedia becomes one of the largest knowledge bases on the Web. It has attracted 513 million page views per day in January 2012. However, one critical issue for Wikipedia is that articles in different language are very unbalanced. For example, the number of articles on Wikipedia in English has reac ..."
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Wikipedia becomes one of the largest knowledge bases on the Web. It has attracted 513 million page views per day in January 2012. However, one critical issue for Wikipedia is that articles in different language are very unbalanced. For example, the number of articles on Wikipedia in English has reached 3.8 million, while the number of Chinese articles is still less than half million and there are only 217 thousand cross-lingual links between articles of the two languages. On the other hand, there are more than 3.9 million Chinese Wi-ki articles on Baidu Baike and Hudong.com, two popular encyclopedias in Chinese. One important question is how to link the knowledge entries distributed in different knowledge bases. This will immensely enrich the information in the on-line knowledge bases and benefit many applications. In this paper, we study the problem of cross-lingual knowledge link-ing and present a linkage factor graph model. Features are defined according to some interesting observations. Exper-iments on the Wikipedia data set show that our approach can achieve a high precision of 85.8 % with a recall of 88.1%. The approach found 202,141 new cross-lingual links between English Wikipedia and Baidu Baike.
Can We Understand van Gogh’s Mood? Learning to Infer Affects from Images in Social Networks
"... Can we understand van Gogh’s mood from his artworks? For many years, people have tried to capture van Gogh’s affects from his artworks so as to understand the essential meaning behind the images and catch on why van Gogh created these works. In this paper, we study the problem of inferring affects f ..."
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Can we understand van Gogh’s mood from his artworks? For many years, people have tried to capture van Gogh’s affects from his artworks so as to understand the essential meaning behind the images and catch on why van Gogh created these works. In this paper, we study the problem of inferring affects from images in social networks. In particular, we aim to answer: What are the fundamental features that reflect the affects of the authors in images? How the social network information can be leveraged to help detect these affects? We propose a semi-supervised framework to formulate the problem into a factor graph model. Experiments on 20,000 random-download Flickr images show that our method can achieve a precision of 49 % with a recall of 24 % on inferring authors ’ affects into 16 categories. Finally, we demonstrate the effectiveness of the proposed method on automatically understanding van Gogh’s Mood from his artworks, and inferring the trend of public affects around special event.
Link Prediction and Recommendation across Heterogeneous Social Networks
"... Abstract—Link prediction and recommendation is a fundamental problem in social network analysis. The key challenge of link prediction comes from the sparsity of networks due to the strong disproportion of links that they have potential to form to links that do form. Most previous work tries to solve ..."
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Abstract—Link prediction and recommendation is a fundamental problem in social network analysis. The key challenge of link prediction comes from the sparsity of networks due to the strong disproportion of links that they have potential to form to links that do form. Most previous work tries to solve the problem in single network, few research focus on capturing the general principles of link formation across heterogeneous networks. In this work, we give a formal definition of link recommendation across heterogeneous networks. Then we propose a ranking factor graph model (RFG) for predicting links in social networks, which effectively improves the predictive performance. Motivated by the intuition that people make friends in different networks with similar principles, we find several social patterns that are general across heterogeneous networks. With the general social patterns, we develop a transfer-based RFG model that combines them with network structure information. This model provides us insight into fundamental principles that drive the link formation and network evolution. Finally, we verify the predictive performance of the presented transfer model on 12 pairs of transfer cases. Our experimental results demonstrate that the transfer of general social patterns indeed help the prediction of links. Keywords-Social network analysis, Link prediction, Recommendation, Factor graph, Heterogeneous networks
Mining social media with social theories: A survey. SIGKDD Explorations
, 2014
"... The increasing popularity of social media encourages more and more users to participate in various online activities and produces data in an unprecedented rate. Social me-dia data is big, linked, noisy, highly unstructured and in-complete, and differs from data in traditional data mining, which cult ..."
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The increasing popularity of social media encourages more and more users to participate in various online activities and produces data in an unprecedented rate. Social me-dia data is big, linked, noisy, highly unstructured and in-complete, and differs from data in traditional data mining, which cultivates a new research field- social media mining. Social theories from social sciences are helpful to explain so-cial phenomena. The scale and properties of social media data are very different from these of data social sciences use to develop social theories. As a new type of social data, social media data has a fundamental question- can we ap-ply social theories to social media data? Recent advances in computer science provide necessary computational tools and techniques for us to verify social theories on large-scale so-cial media data. Social theories have been applied to mining social media. In this article, we review some key social theo-ries in mining social media, their verification approaches, in-teresting findings, and state-of-the-art algorithms. We also discuss some future directions in this active area of mining social media with social theories. 1.
How do your friends on social media disclose your emotions
- In AAAI’14
, 2014
"... Mining emotions hidden in images has attracted signif-icant interest, in particular with the rapid development of social networks. The emotional impact is very impor-tant for understanding the intrinsic meanings of images. Despite many studies have been done, most existing methods focus on image con ..."
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Mining emotions hidden in images has attracted signif-icant interest, in particular with the rapid development of social networks. The emotional impact is very impor-tant for understanding the intrinsic meanings of images. Despite many studies have been done, most existing methods focus on image content, but ignore the emo-tions of the user who has published the image. To un-derstand the emotional impact from images, one inter-esting question is: How does social effect correlate with the emotion expressed in an image? Specifically, can we leverage friends interactions (e.g., discussions) related to an image to help discover the emotions? In this paper, we formally formalize the problem and propose a novel emotion learning method by jointly modeling images posted by social users and comments added by friends. One advantage of the model is that it can distinguish those comments that are closely related to the emotion expression for an image from other irrelevant ones. Ex-periments on an open Flickr dataset show that the pro-posed model can significantly improve (+37.4 % by F1) the accuracy for inferring user emotions. More interest-ingly, we found that half of the improvements are due to interactions between 1 % of the closest friends.
How Long will She Call Me? Distribution, Social Theory and Duration Prediction
"... Abstract. Call duration analysis is a key issue for understanding underlying patterns of (mobile) phone users. In this paper, we study to which extent the duration of a call between users can be predicted in a dynamic mobile network. We have collected a mobile phone call data from a mobile operating ..."
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Abstract. Call duration analysis is a key issue for understanding underlying patterns of (mobile) phone users. In this paper, we study to which extent the duration of a call between users can be predicted in a dynamic mobile network. We have collected a mobile phone call data from a mobile operating company, which results in a network of 272,345 users and 3.9 million call records during two months. We first examine the dynamic distribution properties of the mobile network including periodicity and demographics. Then we study several important social theories in the call network including strong/weak ties, link homophily, opinion leader and social balance. The study reveals several interesting phenomena such as people with strong ties tend to make shorter calls and young females tend to make long calls, in particular in the evening. Finally, we present a time-dependent factor graph model to model and infer the call duration between users, by incorporating our observations in the distribution analysis and the social theory analysis. Experiments show that the presented model can achieve much better predictive performance compared to several baseline methods. Our study offers evidences for social theories and also unveils several different patterns in the call network from online social networks. 1
Active Learning for Networked Data Based on Non-progressive Diffusion Model
"... We study the problem of active learning for networked data, where samples are connected with links and their labels are correlated with each other. We particularly focus on the setting of using the probabilistic graphical model to model the networked data, due to its effectiveness in capturing the d ..."
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We study the problem of active learning for networked data, where samples are connected with links and their labels are correlated with each other. We particularly focus on the setting of using the probabilistic graphical model to model the networked data, due to its effectiveness in capturing the dependency between labels of linked samples. We propose a novel idea of connecting the graphical model to the information diffusion process, and precisely define the active learning problem based on the non-progressive diffu-sion model. We show the NP-hardness of the problem and propose a method called MaxCo to solve it. We derive the lower bound for the optimal solution for the active learn-ing setting, and develop an iterative greedy algorithm with provable approximation guarantees. We also theoretically prove the convergence and correctness of MaxCo. We evaluate MaxCo on four different genres of datasets: Coauthor, Slashdot, Mobile, and Enron. Our experiments show a consistent improvement over other competing ap-proaches.
1Mobility Viewer: An Eulerian Approach for Studying Urban Crowd Flow
"... Abstract—Studying human movement citywide is important for understanding the mobility and transportation patterns. Rather than investigating the trajectories of individuals, we employ an Eulerian approach to analyze the crowd flows among a geographical network and a social network, which are extract ..."
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Abstract—Studying human movement citywide is important for understanding the mobility and transportation patterns. Rather than investigating the trajectories of individuals, we employ an Eulerian approach to analyze the crowd flows among a geographical network and a social network, which are extracted from the mobile phone data. We design a suite of visualization techniques to illustrate the dynamic evolutions of the flow over the networks. We contribute the design and implementation of a visual analytics system, called MobilityViewer, that supports situation-aware understanding and visual reasoning of human mobility. We exemplify our approach with a real citywide dataset of 7 millions users in two months.
Exploiting Sentiment Homophily for Link Prediction
"... Link prediction on social media is an important problem for recommendation systems. Understanding the interplay of users ’ sentiments and social relationships can be potentially valuable. Specifically, we study how to exploit sentiment homophily for link prediction. We evaluate our approach on a dat ..."
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Link prediction on social media is an important problem for recommendation systems. Understanding the interplay of users ’ sentiments and social relationships can be potentially valuable. Specifically, we study how to exploit sentiment homophily for link prediction. We evaluate our approach on a dataset gathered from Twitter that consists of tweets sent in one month during U.S. 2012 political campaign along with the “follows ” relationship between users. Our first con-tribution is defining a set of sentiment-based features that help predict the likelihood of two users becoming “friends” (i.e., mutually mentioning or following each other) based on their sentiments toward topics of mutual interest. Our eval-uation in a supervised learning framework demonstrates the benefits of sentiment-based features in link prediction. We find that Adamic-Adar and Euclidean distance measures are the best predictors. Our second contribution is proposing a factor graph model that incorporates a sentiment-based variant of cognitive balance theory. Our evaluation shows that, when tie strength is not too weak, our model is more effective in link prediction than traditional machine learning techniques.
Transfer Link Prediction across Heterogeneous Social Networks
"... Interpersonal ties are responsible for the structure of social networks and the transmission of informa-tion through these networks. Different types of social ties have essentially different influence on people. Awareness of the types of social ties can benefit many applications such as recommendati ..."
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Interpersonal ties are responsible for the structure of social networks and the transmission of informa-tion through these networks. Different types of social ties have essentially different influence on people. Awareness of the types of social ties can benefit many applications such as recommendation and community detection. For example, our close friends tend to move in the same circles that we do, while our classmates may be distributed into different communities. Though a bulk of research has focused on inferring particular types of relationships in a specific social network, few publications systematically study the generalization of the problem of predicting social ties across multiple heterogeneous networks. In this work, we develop a framework referred to as TranFG for classifying the type of social relationships by learning across heterogeneous networks. The framework incorporates social theories into a factor graph model, which effectively improves the accuracy of predicting the type of social relationships in a target network, by borrowing knowledge from a different source network. We also present several active learning strategies to further enhance the inferring performance. To scale up the model to handle real large networks, we design a distributed learning algorithm for the proposed model. We evaluate the proposed framework (TranFG) on six different networks and compare with several exist-ing methods. TranFG clearly outperforms the existing methods on multiple metrics. For example, by leverag-
