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Link Prediction and Recommendation across Heterogeneous Social Networks
2012
2012 IEEE 12th International Conference on Data Mining
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Then we propose a ranking factor graph model (RFG) for predicting links in social networks, which effectively improves the predictive performance. ...
Our experimental results demonstrate that the transfer of general social patterns indeed help the prediction of links. ...
This work was done when the first author was visiting Tsinghua University. ...
doi:10.1109/icdm.2012.140
dblp:conf/icdm/DongTWTCRC12
fatcat:jojr3sa7bzhwrebu2bfi7wntiy
Understanding Social Networks Using Transfer Learning
2018
Computer
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We compare the performance of TraNet with other approaches and find that our approach can best transfer knowledge on users across platforms in the given tasks. ...
We systematically investigate how the concept of transfer learning may be applied to the study of users on newly created (emerging) Web platforms, and propose our transfer learning-based approach, TraNet ...
Acknowledgements The authors would like to thank Software AG for providing the dataset. ...
doi:10.1109/mc.2018.2701640
fatcat:efuegsdotvhtvg5uq3eksak4ry
You are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors
[article]
2016
arXiv
pre-print
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Our attacks leverage seemingly innocent user information that is publicly available in online social networks to infer missing attributes of targeted users. ...
., locations, occupations, and interests) of online social network users. ...
Acknowledgements We would like to thank the anonymous reviewers for their insightful feedback. ...
arXiv:1606.05893v1
fatcat:5vguqtows5eqpjfjslwbigj37m
Transfer Learning to Infer Social Ties across Heterogeneous Networks
2016
ACM Transactions on Information Systems
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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 types of social relationships in a target network by borrowing knowledge ...
Zhang et al. [2013] studied the problem of link prediction for new users across aligned heterogeneous social networks. ...
doi:10.1145/2746230
fatcat:32cf7wnewnc23gzypajmdfhghq
User Identification Across Social Media
2015
ACM Transactions on Knowledge Discovery from Data
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This study paves the way for analysis and mining across social networking sites, and facilitates the creation of novel online services across sites. ...
In particular, recommending friends and advertising across networks, analyzing information diffusion across sites, and studying specific user behavior such as user migration across sites in social media ...
Social network S 1 consists of the nodes on the left and social network S 2 consists of the nodes on the right. ...
doi:10.1145/2747880
fatcat:5yonxyqntncc7geaf67a7israe
Identifying social roles in reddit using network structure
2014
Proceedings of the 23rd International Conference on World Wide Web - WWW '14 Companion
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To this end, we explore user posting behavior on reddit, a large social networking site comprised of many sub-communities in which a user may participate simultaneously. ...
As social networks and the user-generated content that populates them continue to grow in prevalence, size, and influence, understanding how users interact and produce this content becomes increasingly ...
While a significant portion of existing social networking literature is applicable to these new spaces, much of it assumes consistent user behavior throughout the network. ...
doi:10.1145/2567948.2579231
dblp:conf/www/BuntainG14
fatcat:h5c5zy7q2rewzcwitij2ddf2hu
Social behavior prediction with graph U-Net+
2021
Discover Internet of Things
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AbstractWe focus on the problem of predicting social media user's future behavior and consider it as a graph node binary classification task. ...
In this paper, we follow the fact that social media users have influence on their neighbor area, and extract subgraph structures from real-world social networks. ...
Because our goal is to predict the future behavior of these target ego-users, so we focus on the scope of target node and its local neighborhood. ...
doi:10.1007/s43926-021-00018-3
fatcat:f7srl4pb2vd43cty32wcydbpzy
User Identity Linkage across Online Social Networks
2017
SIGKDD Explorations
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We also discuss related research areas, open problems, and future research directions for user identity linkage across online social networks. ...
In this paper, we review key achievements of user identity linkage across online social networks including stateof-the-art algorithms, evaluation metrics, and representative datasets. ...
The linkage of user identities across different social media sites provides a great chance to study use migration behaviors. In addition, linking user identities allows for: I. ...
doi:10.1145/3068777.3068781
fatcat:zuh6dpjzarcf3ftbdx7k7o6wqy
MIDMod-OSN: A Microscopic-level Information Diffusion Model for Online Social Networks
[article]
2020
arXiv
pre-print
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Our findings show that followers play an important role in the diffusion process and it is possible to use the diffusion and OSN behavior of users for predicting the trending character of a message without ...
As online social networks continue to be commonly used for the dissemination of information to the public, understanding the phenomena that govern information diffusion is crucial for many security and ...
Future work may include more complex prediction tasks, involving the use of latent user and message attributes for predicting user reactions to posts based on the user's perceived veracity of the post ...
arXiv:2002.10522v2
fatcat:vwrzg3cy4zgovfnuserxr4tj5a
Social Network Link Prediction using Semantics Deep Learning
2018
International Journal of Advanced Computer Science and Applications
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It has pulled the consideration of several analysts as a powerful system to be utilized as a part of social networks study to understand the relations between nodes in social circles. ...
Currently, social networks have brought about an enormous number of users connecting to such systems over a couple of years, whereas the link mining is a key research track in this area. ...
CONCLUSION The theme of this work is to exploit Social Networks for prediction of nature of relationships among users that are not directly connected. ...
doi:10.14569/ijacsa.2018.090138
fatcat:27udflk2mrg4hg4gfyldrkgr5e
Network Model Selection for Task-Focused Attributed Network Inference
[article]
2017
arXiv
pre-print
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We demonstrate our methodology on three online user activity datasets and show that network model selection for the appropriate network task vs. an alternate task increases performance by an order of magnitude ...
Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). ...
We learn joint relationships between node attribute vectors and some target label (e.g. node behavior) of interest. ...
arXiv:1708.06303v2
fatcat:vbadmkkyhbfn5h6eiooaimhvxi
Inferring social ties across heterogenous networks
2012
Proceedings of the fifth ACM international conference on Web search and data mining - WSDM '12
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In this work, we develop a framework for classifying the type of social relationships by learning across heterogeneous networks. ...
The framework incorporates social theories into a machine learning model, which effectively improves the accuracy of inferring the type of social relationships in a target network, by borrowing knowledge ...
The model incorporates social theories into a semi-supervised learning framework, which is used to transfer supervised information from the source network to help infer social ties in the target network ...
doi:10.1145/2124295.2124382
dblp:conf/wsdm/TangLK12
fatcat:h6iimswxafhe3pysatnrlzyede
Prediction of purchase behaviors across heterogeneous social networks
2015
Journal of Supercomputing
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The results show that our method identifies up to 70 % of the correlation accounts between Facebook and eBay, one of the most popular social network sites and online shopping Prediction of purchase behaviors ...
Using the above account relationships, we then put forward a predicting method that combines heterogeneous social network information and online shopping information, to predict the purchasing behaviors ...
Matrix Factorization (or MF) [27] is one of the common methods for model-based recommendation. MF has been proposed to perform predictions for a single user-item rating matrix. ...
doi:10.1007/s11227-015-1495-8
fatcat:bcw5jcxnbjconjbshcbxbih6ai
Connecting personal-scale sensing and networked community behavior to infer human activities
2014
Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '14 Adjunct
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We propose the Networked Community Behavior (NCB) framework for activity recognition, specifically designed to exploit community-scale behavioral patterns. ...
Under NCB, patterns of community behavior are mined to identify social connections that can cause correlated behavior, this information is used to augment sensor-based inferences available from the actions ...
All adjacent nodes of this user node (i.e., other users with whom the target user is socially connected) will be included in the neighborhood, along with all of their children activities nodes. ...
doi:10.1145/2632048.2636094
dblp:conf/huc/LanePZZ14
fatcat:tpkci4jvz5gmdb3l7tl36xe6ya
Link prediction in social networks: the state-of-the-art
2014
Science China Information Sciences
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In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. ...
The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks. ...
walk is more likely to visit target nodes than other nodes of the network. ...
doi:10.1007/s11432-014-5237-y
fatcat:x6jd4zwg7fgefjrho4en4rtfgm
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