Similar but foreign: Link recommendation across communities

2021 ◽  
Vol 552 ◽  
pp. 142-166
Author(s):  
Chunyao Song ◽  
Yao Ge ◽  
Tingjian Ge ◽  
Haixia Wu ◽  
Zhutian Lin ◽  
...  
Keyword(s):  
Link Recommendation

A Survey of Link Recommendation for Social Networks

2018 ◽  
Vol 9 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Zhepeng (Lionel) Li ◽  
Xiao Fang ◽  
Olivia R. Liu Sheng
Keyword(s):  
Social Networks ◽  
Link Recommendation

scholarly journals Link recommendation algorithms and dynamics of polarization in online social networks

2021 ◽  
Vol 118 (50) ◽  
pp. e2102141118 ◽  
Author(s):  
Fernando P. Santos ◽  
Yphtach Lelkes ◽  
Simon A. Levin
Keyword(s):  
Social Networks ◽  
Complex Adaptive Systems ◽  
Online Social Networks ◽  
Adaptive Systems ◽  
Structural Similarity ◽  
Recommendation Algorithms ◽  
Complex Adaptive ◽  
Network Topologies ◽  
Group Members ◽  
Link Recommendation

The level of antagonism between political groups has risen in the past years. Supporters of a given party increasingly dislike members of the opposing group and avoid intergroup interactions, leading to homophilic social networks. While new connections offline are driven largely by human decisions, new connections on online social platforms are intermediated by link recommendation algorithms, e.g., “People you may know” or “Whom to follow” suggestions. The long-term impacts of link recommendation in polarization are unclear, particularly as exposure to opposing viewpoints has a dual effect: Connections with out-group members can lead to opinion convergence and prevent group polarization or further separate opinions. Here, we provide a complex adaptive–systems perspective on the effects of link recommendation algorithms. While several models justify polarization through rewiring based on opinion similarity, here we explain it through rewiring grounded in structural similarity—defined as similarity based on network properties. We observe that preferentially establishing links with structurally similar nodes (i.e., sharing many neighbors) results in network topologies that are amenable to opinion polarization. Hence, polarization occurs not because of a desire to shield oneself from disagreeable attitudes but, instead, due to the creation of inadvertent echo chambers. When networks are composed of nodes that react differently to out-group contacts, either converging or polarizing, we find that connecting structurally dissimilar nodes moderates opinions. Overall, our study sheds light on the impacts of social-network algorithms and unveils avenues to steer dynamics of radicalization and polarization in online social networks.

scholarly journals Recommending Links to Maximize the Influence in Social Networks

2019 ◽  
Author(s):  
Federico Corò ◽  
Gianlorenzo D'Angelo ◽  
Yllka Velaj
Keyword(s):  
Social Media ◽  
Social Influence ◽  
Fixed Number ◽  
Constant Factor ◽  
Computational Time ◽  
Recommendation Algorithm ◽  
The Social ◽  
Social Link ◽  
Social Biases ◽  
Link Recommendation

Social link recommendation systems, like "People-you-may-know" on Facebook, "Who-to-follow" on Twitter, and "Suggested-Accounts" on Instagram assist the users of a social network in establishing new connections with other users. While these systems are becoming more and more important in the growth of social media, they tend to increase the popularity of users that are already popular. Indeed, since link recommenders aim at predicting users' behavior, they accelerate the creation of links that are likely to be created in the future, and, as a consequence, they reinforce social biases by suggesting few (popular) users, while giving few chances to the majority of users to build new connections and increase their popularity.In this paper we measure the popularity of a user by means of its social influence, which is its capability to influence other users' opinions, and we propose a link recommendation algorithm that evaluates the links to suggest according to their increment in social influence instead of their likelihood of being created. In detail, we give a constant factor approximation algorithm for the problem of maximizing the social influence of a given set of target users by suggesting a fixed number of new connections. We experimentally show that, with few new links and small computational time, our algorithm is able to increase by far the social influence of the target users. We compare our algorithm with several baselines and show that it is the most effective one in terms of increased influence.

scholarly journals The Network Visibility Problem

2022 ◽  
Vol 40 (2) ◽  
pp. 1-42
Author(s):  
Khashayar Gatmiry ◽  
Manuel Gomez-Rodriguez
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Greedy Algorithm ◽  
Real Data ◽  
Recommendation Algorithm ◽  
Attention Economy ◽  
Set Function ◽  
Link Recommendation ◽  
The Greedy Algorithm

Social media is an attention economy where broadcasters are constantly competing for attention in their followers’ feeds. Broadcasters are likely to elicit greater attention from their followers if their posts remain visible at the top of their followers’ feeds for a longer period of time. However, this depends on the rate at which their followers receive information in their feeds, which in turn depends on the broadcasters they follow. Motivated by this observation and recent calls for fairness of exposure in social networks, in this article, we look at the task of recommending links from the perspective of visibility optimization. Given a set of candidate links provided by a link recommendation algorithm, our goal is to find a subset of those links that would provide the highest visibility to a set of broadcasters. To this end, we first show that this problem reduces to maximizing a nonsubmodular nondecreasing set function under matroid constraints. Then, we show that the set function satisfies a notion of approximate submodularity that allows the standard greedy algorithm to enjoy theoretical guarantees. Experiments on both synthetic and real data gathered from Twitter show that the greedy algorithm is able to consistently outperform several competitive baselines.

Sign in / Sign up

Export Citation Format

Share Document