Using Friendship Ties and Family Circles for Link Prediction

@inproceedings{Zheleva2008UsingFT,
  title={Using Friendship Ties and Family Circles for Link Prediction},
  author={E. Zheleva and L. Getoor and J. Golbeck and U. Kuter},
  booktitle={SNAKDD},
  year={2008}
}
Social networks can capture a variety of relationships among the participants. Both friendship and family ties are commonly studied, but most existing work studies them in isolation. Here, we investigate how these networks can be overlaid, and propose a feature taxonomy for link prediction. We show that when there are tightly-knit family circles in a social network, we can improve the accuracy of link prediction models. This is done by making use of the family circle features based on the… Expand
Fast and accurate link prediction in social networking systems
TLDR
This paper provides friend recommendations, also known as the link prediction problem, by traversing all paths of a limited length, based on the ''algorithmic small world hypothesis'' and is able to provide more accurate and faster friend recommendations. Expand
An Efficient Link Prediction Technique in Social Networks based on Node Neighborhoods
TLDR
The results on three real social network datasets show that the novel LinkGyp link prediction technique yields more accurate results than several existing link prediction techniques. Expand
A naïve Bayes model based on overlapping groups for link prediction in online social networks
TLDR
A new approach is proposed that uses overlapping groups structural information for building a naïve Bayes model and shows three different measures derived from the common neighbors that help to improve the link prediction accuracy. Expand
Investigating Link Inference in Partially Observable Networks: Friendship Ties and Interaction
TLDR
The results suggest that interactions reiterate the information contained in friendship ties sufficiently well to serve as a proxy when the majority of a network is unobserved. Expand
Node-Pair Feature Extraction for Link Prediction
  • T. Feyessa, M. Bikdash, G. Lebby
  • Computer Science
  • 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing
  • 2011
TLDR
This work uses a back propagation neural network to predict existence or emergence of a link between pairs of nodes using node pair properties such as reciprocity, transitivity and shared neighbors. Expand
Inferring Unobservable Inter-community Links in Large Social Networks
TLDR
This is the first method that infers links that exist but are unobservable in a phone call-based social network and performs the inference at the community level, where the discovery of unobserved inter-community communication can provide further insight into the organizational structure of the social network. Expand
PLUMS: Predicting Links Using Multiple Sources
TLDR
A robust and effective classifier for link prediction using multiple auxiliary networks, that does not require any explicit feature construction, and can be personalized to each user based on the past accept and reject behavior is developed. Expand
Confidence-based Link/Attribute Inference based on Friendship Circles
TLDR
This paper discusses how to determine the appropriate order when recommending new friends or new interests based on the confidence scores generated by using SVM on a social network, and shows that the accuracy achieved when using the confidence score order is better than that with a fixed order. Expand
A review of similarity measures and link prediction models in social networks
  • S Hemkiran et. al.
  • Computer Science
  • 2020
TLDR
This study presents a concise review of the similarity measures, techniques employed in predicting future links and application of link prediction with emphasis on dynamic networks. Expand
Detecting partnership in location-based and online social networks
TLDR
It is established that location-based social networks and correspondingly induced features based on events attended by users could identify partnership with 0.922 AUC, while online social network data had a classification power of 0.892 AUC. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 17 REFERENCES
The dynamics of Web-based social networks: Membership, relationships, and change
TLDR
The first comprehensive survey of Web-based social networks is presented, followed by an analysis of membership and relationship dynamics within them, and several conclusions on how users behave in social networks are presented. Expand
Link prediction using supervised learning
TLDR
This research identifies a set of features that are key to the superior performance under the supervised learning setup, and shows that a small subset of features always plays a significant role in the link prediction job. Expand
Friends and neighbors on the Web
TLDR
It is shown that some factors are better indicators of social connections than others, and that these indicators vary between user populations. Expand
Stochastic link and group detection
TLDR
A probabilistic model of link generation based on membership in groups is proposed that considers both observed link evidence and demographic information about the entities and shows several heuristics that make the search tractable. Expand
cGraph: A Fast Graph-Based Method for Link Analysis and Queries
TLDR
This work quantitatively compares this representation and learning method against other algorithms on the task of predicting future links and new “friendships” in a variety of real world data sets. Expand
Evolution of the social network of scientific collaborations
TLDR
The results indicate that the co-authorship network of scientists is scale-free, and that the network evolution is governed by preferential attachment, affecting both internal and external links, and a simple model is proposed that captures the network's time evolution. Expand
Link Prediction in Relational Data
TLDR
It is shown that the collective classification approach of RMNs, and the introduction of subgraph patterns over link labels, provide significant improvements in accuracy over flat classification, which attempts to predict each link in isolation. Expand
Statistical Relational Learning for Link Prediction
TLDR
This paper proposes an integrated approach to building regression models from data stored in relational databases in which potential predictors are generated by structured search of the space of queries to the database, and then tested for inclusion in a logistic regression. Expand
Who is the best connected scientist? A study of scientific coauthorship networks
Using data from computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of theseExpand
Finding tribes: identifying close-knit individuals from employment patterns
TLDR
A family of algorithms is presented to uncover tribes-groups of individuals who share unusual sequences of affiliations, which contain individuals at higher risk for fraud, are homogenous with respect to risk scores, and are geographically mobile. Expand
...
1
2
...