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Infer User Interests via Link Structure Regularization
Jinpeng Wang, Wayne Xin Zhao, Yulan He, Xiaoming Li
Article No.: 23
Learning user interests from online social networks helps to better understand user behaviors and provides useful guidance to design user-centric applications. Apart from analyzing users' online content, it is also important to consider users'...
Cluster-Based Collaborative Filtering for Sign Prediction in Social Networks with Positive and Negative Links
Amin Javari, Mahdi Jalili
Article No.: 24
Social network analysis and mining get ever-increasingly important in recent years, which is mainly due to the availability of large datasets and advances in computing systems. A class of social networks is those with positive and negative links....
Given a social graph, the problem of influence maximization is to determine a set of nodes that maximizes the spread of influences. While some recent research has studied the problem of influence maximization, these works are generally too time...
Structure and Overlaps of Ground-Truth Communities in Networks
Jaewon Yang, Jure Leskovec
Article No.: 26
One of the main organizing principles in real-world networks is that of network communities, where sets of nodes organize into densely linked clusters. Even though detection of such communities is of great interest, understanding the...
Joint Link Prediction and Attribute Inference Using a Social-Attribute Network
Neil Zhenqiang Gong, Ameet Talwalkar, Lester Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine (Runting) Shi, Dawn Song
Article No.: 27
The effects of social influence and homophily suggest that both network structure and node-attribute information should inform the tasks of link prediction and node-attribute inference. Recently, Yin et al. [2010a, 2010b] proposed an...
Traditional approaches to community detection, as studied by physicists, sociologists, and more recently computer scientists, aim at simply partitioning the social network graph. However, with the advent of online social networking sites, richer...
Learning Probabilistic Hierarchical Task Networks as Probabilistic Context-Free Grammars to Capture User Preferences
Nan Li, William Cushing, Subbarao Kambhampati, Sungwook Yoon
Article No.: 29
We introduce an algorithm to automatically learn probabilistic hierarchical task networks (pHTNs) that capture a user's preferences on plans by observing only the user's behavior. HTNs are a common choice of representation for a variety of...
A Framework for Effectively Choosing between Alternative Candidate Partners
Shulamit Reches, Meir Kalech, Philip Hendrix
Article No.: 30
Many multi-agent settings require that agents identify appropriate partners or teammates with whom to work on tasks. When selecting potential partners, agents may benefit from obtaining information about the alternatives, for instance, through...
In the field of visual art, metaphor is a way to communicate meaning to the viewer. We present a computational system for communicating visual metaphor that can identify adjectives for describing an image based on a low-level visual feature...
Mining Check-In History for Personalized Location Naming
Defu Lian, Xing Xie
Article No.: 32
Many innovative location-based services have been established to offer users greater convenience in their everyday lives. These services usually cannot map user's physical locations into semantic names automatically. The semantic names of...
Web portal services have become an important medium to deliver digital content (e.g. news, advertisements, etc.) to Web users in a timely fashion. To attract more users to various content modules on the Web portal, it is necessary to design a...
AutoLCA: A Framework for Sustainable Redesign and Assessment of Products
M. Shahriar Hossain, Manish Marwah, Amip Shah, Naren Ramakrishnan, Layne T. Watson
Article No.: 34
With increasing public consciousness regarding sustainability, companies are ever more eager to introduce eco-friendly products and services. Assessing environmental footprints and designing sustainable products are challenging tasks since they...
Multi-label classification refers to the task of predicting potentially multiple labels for a given instance. Conventional multi-label classification approaches focus on single objective setting, where the learning algorithm optimizes over a...
Detecting Social Media Hidden Communities Using Dynamic Stochastic Blockmodel with Temporal Dirichlet Process
Xuning Tang, Christopher C. Yang
Article No.: 36
Detecting evolving hidden communities within dynamic social networks has attracted significant attention recently due to its broad applications in e-commerce, online social media, security intelligence, public health, and other areas. Many...