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Many social networks are characterized by actors (nodes) holding quantitative opinions about movies, songs, sports, people, colleges, politicians, and so on. These opinions are influenced by network neighbors. Many models have been proposed for such opinion dynamics, but they have some limitations. Most consider the strength of edge influence as fixed. Some(More)
Despite the success of large knowledge bases, one kind of knowledge that has not received attention so far is that of human activities. An example of such an activity is proposing to someone (to get married). For the computer, knowing that this involves two adults, often but not necessarily a woman and a man, that it often takes place in some romantic(More)
In the <i>Recommendation Problem</i>, it is often important to find a set of items <i>similar</i> to a particular item or a group of items. This problem of finding similar items for the recommendation task may also be viewed as a link prediction problem in a network, where the items can be treated as the nodes. The strength of the edge connecting two items(More)
Social media and social networking sites have become a global pinboard for exposition and discussion of news, topics, and ideas, where social media users often form their opinion about a particular topic by learning information about it from her peers. In this context, whenever a user posts a message about a topic, we observe a noisy estimate of her current(More)
With the success of large knowledge graphs, research on automatically acquiring commonsense knowledge is revived. One kind of knowledge that has not received attention is that of human activities. This paper presents an information extraction pipeline for systematically distilling activity knowledge from a corpus of movie scripts. Our semantic frames(More)
A link prediction (LP) algorithm is given a graph, and has to rank, for each node, other nodes that are candidates for new linkage. LP is strongly motivated by social search and recommendation applications. LP techniques often focus on global properties (graph conductance, hitting or commute times, Katz score) or local properties (Adamic-Adar and many(More)
Predicting plausible links that may emerge between pairs of nodes is an important task in social network analysis, with over a decade of active research. Here, we propose a novel framework for link prediction. It integrates signals from node features, the existing local link neighborhood of a node pair, community-level link density, and global graph(More)
Predicting the popularity dynamics of Twitter hashtags has a broad spectrum of applications. Existing works have primarily focused on modeling the popularity of individual tweets rather than the underlying hashtags. As a result, they fail to consider several realistic factors contributing to hashtag popularity. In this paper, we propose Large Margin Point(More)
User engagement in social networks depends critically on the number of online actions their users take in the network. Can we design an algorithm that finds when to incentivize users to take actions to maximize the overall activity in a social network? In this paper, we model the number of online actions over time using multidimensional Hawkes processes,(More)
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