Results 1 - 10
of
10
Meta-path based Multi-Network Collective Link Prediction
"... Online social networks o↵ering various services have become ubiquitous in our daily life. Meanwhile, users nowadays are usually involved in multiple online social networks simulta-neously to enjoy specific services provided by di↵erent net-works. Formally, social networks that share some common user ..."
Abstract
-
Cited by 6 (3 self)
- Add to MetaCart
(Show Context)
Online social networks o↵ering various services have become ubiquitous in our daily life. Meanwhile, users nowadays are usually involved in multiple online social networks simulta-neously to enjoy specific services provided by di↵erent net-works. Formally, social networks that share some common users are named as partially aligned networks. In this paper, we want to predict the formation of social links in multiple partially aligned social networks at the same time, which is formally defined as the multi-network link (formation) pre-diction problem. In multiple partially aligned social net-works, users can be extensively correlated with each other by various connections. To categorize these diverse connec-tions among users, 7 “intra-network social meta paths ” and 4 categories of “inter-network social meta paths ” are pro-
Tripartite graph clustering for dynamic sentiment analysis on social media
, 2014
"... The growing popularity of social media (e.g., Twitter) allows users to easily share information with each other and influence others by expressing their own sentiments on various subjects. In this work, we propose an unsupervised tri-clustering framework, which analyzes both user-level and tweet-lev ..."
Abstract
-
Cited by 3 (3 self)
- Add to MetaCart
The growing popularity of social media (e.g., Twitter) allows users to easily share information with each other and influence others by expressing their own sentiments on various subjects. In this work, we propose an unsupervised tri-clustering framework, which analyzes both user-level and tweet-level sentiments through co-clustering of a tripartite graph. A compelling feature of the pro-posed framework is that the quality of sentiment clustering of tweets, users, and features can be mutually improved by joint clustering. We further investigate the evolution of user-level sentiments and la-tent feature vectors in an online framework and devise an efficient online algorithm to sequentially update the clustering of tweets, users and features with newly arrived data. The online framework not only provides better quality of both dynamic user-level and tweet-level sentiment analysis, but also improves the computational and storage efficiency. We verified the effectiveness and efficiency of the proposed approaches on the November 2012 California bal-lot Twitter data. 1.
Modelling and Analysis of Vertical Rotary Automated Drilling Fixture, HCTL Open International Journal of Technology Innovations and Research (IJTIR), Volume 16, July 2015, e-ISSN: 2321-1814, ISBN: 978-1-943730-43-8. This article is an open access article
"... Project work is with the development of process automation equipment in heavy machineries. Work giving perfect solution to make feasible multiple operation in single machine and with single operator. Indexing bracket made by welding structure is validate with the loading conditions expecting ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
Project work is with the development of process automation equipment in heavy machineries. Work giving perfect solution to make feasible multiple operation in single machine and with single operator. Indexing bracket made by welding structure is validate with the loading conditions expecting in the process as the 4 numbers of fixtures are going to mount on the bracket simultaneously and 3 process will be carried out on 3 stations that means 3 fixtures will be engaged with loading.the solution is withstanding on boundary conditions all are expected. Mechanical working behaviour also considered while designing this bracket. Stability and anti bending gusseting considered as per the analysis results gets changes. Assembly is made with optimised solution by making validation on optimised components before finalising the same.
Negative Link Prediction in Social Media
"... Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed net-work analysis suggests that negative links have added value in the analytical process. A major impediment in their effec-tive use is that most social media sites do not enable u ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
(Show Context)
Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed net-work analysis suggests that negative links have added value in the analytical process. A major impediment in their effec-tive use is that most social media sites do not enable users to specify them explicitly. In other words, a gap exists be-tween the importance of negative links and their availability in real data sets. Therefore, it is natural to explore whether one can predict negative links automatically from the com-monly available social network data. In this paper, we in-vestigate the novel problem of negative link prediction with only positive links and content-centric interactions in social media. We make a number of important observations about negative links, and propose a principled framework NeLP, which can exploit positive links and content-centric interac-tions to predict negative links. Our experimental results on real-world social networks demonstrate that the proposed NeLP framework can accurately predict negative links with positive links and content-centric interactions. Our detailed experiments also illustrate the relative importance of various factors to the effectiveness of the proposed framework.
A Visibility-based Model for Link Prediction in Social Media
"... A core task of social network analysis is to predict the for-mation of new social links. In the context of social media, link prediction serves as the foundation for forecasting the evolution of the follower graph and predicting interactions and the flow of information between users. Previous link p ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
(Show Context)
A core task of social network analysis is to predict the for-mation of new social links. In the context of social media, link prediction serves as the foundation for forecasting the evolution of the follower graph and predicting interactions and the flow of information between users. Previous link prediction methods have generally represented the social network as a graph and leveraged topological and seman-tic measures of similarity between two nodes to estimate the probability of a new link between them. In this work, we suggest another link creation mechanism for social me-dia that is based on the ease of discovering the new node. Specifically, a user v creates a link to another user u af-ter seeing u’s name on his or her screen; in other words, visibility of a user (name) is a necessary condition for new link formation. We propose a model for link prediction, which estimates the probability a user will see another user’s name, and use this model to predict new links. We estimate a set of parameters in the proposed model using Maximum-Likelihood and Minorize-Maximize methods. Empirical re-sults show that the proposed model can more accurately predict both follow and co-mention links than alternative state-of-the-art methods. Our work suggests that the effort required to discover a new social contact is negatively cor-related with link formation, and the easier it is to discover a user, the higher the likelihood a link will be created. 1
Polarity Related Influence Maximization in Signed Social Networks
, 2014
"... Influence maximization in social networks has been widely studied motivated by applications like spread of ideas or innovations in a network and viral marketing of products. Current studies focus almost exclusively on unsigned social networks containing only positive relationships (e.g. friend or tr ..."
Abstract
- Add to MetaCart
(Show Context)
Influence maximization in social networks has been widely studied motivated by applications like spread of ideas or innovations in a network and viral marketing of products. Current studies focus almost exclusively on unsigned social networks containing only positive relationships (e.g. friend or trust) between users. Influence maximization in signed social networks containing both positive relationships and negative relationships (e.g. foe or distrust) between users is still a challenging problem that has not been studied. Thus, in this paper, we propose the polarity-related influence maximization (PRIM) problem which aims to find the seed node set with maximum positive influence or maximum negative influence in signed social networks. To address the PRIM problem, we first extend the standard Independent Cascade (IC) model to the signed social networks and propose a Polarity-related Independent Cascade (named IC-P) diffusion model. We prove that the influence function of the PRIM problem under the IC-P model is monotonic and submodular Thus, a greedy algorithm can be used to achieve an approximation ratio of 1-1/e for solving the PRIM problem in signed social networks. Experimental results on two signed social network datasets, Epinions and Slashdot, validate that our approximation algorithm for solving the PRIM problem outperforms state-of-the-art methods.
Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization
"... We study temporal link prediction problem, where, given past interactions, our goal is to predict new interactions. We propose a dynamic link prediction method based on non-negative matrix factorization. This method assumes that interactions are more likely between users that are similar to each oth ..."
Abstract
- Add to MetaCart
(Show Context)
We study temporal link prediction problem, where, given past interactions, our goal is to predict new interactions. We propose a dynamic link prediction method based on non-negative matrix factorization. This method assumes that interactions are more likely between users that are similar to each other in the latent space representation. We pro-pose a global optimization algorithm to effectively learn the temporal latent space with quadratic convergence rate and bounded error. In addition, we propose two alternative al-gorithms with local and incremental updates, which provide much better scalability without deteriorating prediction ac-curacy. We evaluate our model on a number of real-world dynamic networks and demonstrate that our model signif-icantly outperforms existing approaches for temporal link prediction in terms of both scalability and predictive power. 1.
Predictability of Distrust with Interaction Data
"... Trust plays a crucial role in helping users collect reliable in-formation in an online world, and has attracted more and more attention in research communities lately. As a con-ceptual counterpart of trust, distrust can be as important as trust. However, distrust is rarely studied in social me-dia b ..."
Abstract
- Add to MetaCart
(Show Context)
Trust plays a crucial role in helping users collect reliable in-formation in an online world, and has attracted more and more attention in research communities lately. As a con-ceptual counterpart of trust, distrust can be as important as trust. However, distrust is rarely studied in social me-dia because distrust information is usually unavailable. The value of distrust has been widely recognized in social sciences and recent work shows that distrust can benefit various on-line applications in social media. In this work, we investi-gate whether we can obtain distrust information via learn-ing when it is not directly available, and propose to study a novel problem- predicting distrust using pervasively avail-able interaction data in an online world. In particular, we analyze interaction data, provide a principled way to mathe-matically incorporate interaction data in a novel framework dTrust to predict distrust information. Experimental results using real-world data show that distrust information is pre-dictable with interaction data by the proposed framework dTrust. Further experiments are conducted to gain a deep understand on which factors contribute to the effectiveness of the proposed framework.
RESEARCH ARTICLE Predicting Positive and Negative Relationships in Large Social Networks
"... In a social network, users hold and express positive and negative attitudes (e.g. support/ opposition) towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships ..."
Abstract
- Add to MetaCart
(Show Context)
In a social network, users hold and express positive and negative attitudes (e.g. support/ opposition) towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw research-ers ' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deterio-rates. Accordingly, in the training phase, we propose a segment-based training framework to divide the training data into two subsets according to the number of common neighbors between users, and build a prediction model for each subset based on support vector machine (SVM). Moreover, to deal with large-scale social network data, we employ a sam-pling strategy that selects small amount of training data while maintaining high accuracy of prediction. We compare our algorithm with traditional algorithms and adaptive boosting of them. Experimental results of typical data sets show that our algorithm can deal with large social networks and consistently outperforms other methods.
