RePBubLik: Reducing Polarized Bubble Radius with Link Insertions

Haddadan, Shahrzad; Menghini, Cristina; Riondato, Matteo; Upfal, Eli

doi:10.1145/3437963.3441825

Cited by 24 publications

(16 citation statements)

References 54 publications

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“…(12) Wikipedia is based on Wikipedia pages and hyperlinks between them [29]. (13) Youtube is a social network on a video platform [30]. (14) Delicious is based on a network of a bookmark website [55].…”

Section: Methodsmentioning

confidence: 99%

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Temporal Walk Centrality: Ranking Nodes in Evolving Networks

Oettershagen¹,

Mutzel²,

Kriege³

2022

Preprint

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We propose the Temporal Walk Centrality, which quantifies the importance of a node by measuring its ability to obtain and distribute information in a temporal network. In contrast to the widely-used betweenness centrality, we assume that information does not necessarily spread on shortest paths but on temporal random walks that satisfy the time constraints of the network. We show that temporal walk centrality can identify nodes playing central roles in dissemination processes that might not be detected by related betweenness concepts and other common static and temporal centrality measures. We propose exact and approximation algorithms with different running times depending on the properties of the temporal network and parameters of our new centrality measure. A technical contribution is a general approach to lift existing algebraic methods for counting walks in static networks to temporal networks. Our experiments on real-world temporal networks show the efficiency and accuracy of our algorithms. Finally, we demonstrate that the rankings by temporal walk centrality often differ significantly from those of other state-of-the-art temporal centralities. CCS CONCEPTS• Information systems → Social networks; • Theory of computation → Graph algorithms analysis.

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Section: Methodsmentioning

confidence: 99%

“…In [7] and [37], the authors introduce top-𝑘 algorithms for temporal closeness variants. The authors of [13] introduce a closeness variant based on bounded random-walks. Related to the considered node importance is the concept of influence in social networks, which has been studied extensively, see, e.g., [19,58] and references therein.…”

Section: Related Workmentioning

confidence: 99%

Temporal Walk Centrality: Ranking Nodes in Evolving Networks

Oettershagen¹,

Mutzel²,

Kriege³

2022

Preprint

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“…In contrast to both Matakos et al (2017) and Rahaman and Hosein (2021), we fix the population's opinions while allowing the network structure to be perturbed. We note also that this paper differs from Garimella et al (2017) and Haddadan et al (2021) in our use of both a distinct measure of polarization and a model of opinion dynamics. Finally, we improve upon the related work of Chen et al (2018) through a more detailed theoretical analysis and use of larger networks.…”

Section: Introductionmentioning

confidence: 92%

“…There has been a significant research effort towards reducing polarization in networks (Chen et al, 2018;Garimella et al, 2017;Haddadan et al, 2021;Matakos et al, 2017;Rahaman and Hosein, 2021). In contrast to both Matakos et al (2017) and Rahaman and Hosein (2021), we fix the population's opinions while allowing the network structure to be perturbed.…”

Section: Introductionmentioning

confidence: 99%

Towards Consensus: Reducing Polarization by Perturbing Social Networks

Rácz¹,

Rigobon²

2022

Preprint

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This paper studies how a centralized planner can modify the structure of a social or information network to reduce polarization. First, polarization is found to be highly dependent on degree and structural properties of the network. We then formulate the planner's problem under full information, and motivate disagreement-seeking and coordinate descent heuristics. A novel setting for the planner in which the population's innate opinions are adversarially chosen is introduced, and shown to be equivalent to maximization of the Laplacian's spectral gap. We prove bounds for the effectiveness of a strategy that adds edges between vertices on opposite sides of the cut induced by the spectral gap's eigenvector. Finally, these strategies are evaluated on six real-world and synthetic networks. In several networks, we find that polarization can be significantly reduced through the addition of a small number of edges.

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“…These works focus on diversity of content consumption, showing that it can be increased by natural collaborative filtering based recommendations. Other works propose remedies to 'fix' recommender systems to limit their polarizing effect [CKSV19,HMRU21]. It would be interesting to understand the effects of these remedies in our opinion and edge dynamics model.…”

Section: Related Workmentioning

confidence: 99%

Local Edge Dynamics and Opinion Polarization

Bhalla¹,

Lechowicz²,

Musco³

2021

Preprint

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The proliferation of social media platforms, recommender systems, and their joint societal impacts have prompted significant interest in opinion formation and evolution within social networks. In this work, we study how local dynamics in a network can drive opinion polarization. In particular, we study time evolving networks under the classic Friedkin-Johnsen opinion model. Edges are iteratively added or deleted according to simple local rules, modeling decisions based on individual preferences and network recommendations. 1 We give theoretical bounds showing how individual edge updates affect polarization, and a related measure of disagreement across edges. Via simulations on synthetic and real-world graphs, we find that the presence of two simple dynamics gives rise to high polarization: 1) confirmation bias -i.e., the preference for nodes to connect to other nodes with similar expressed opinions and 2) friend-of-friend link recommendations, which encourage new connections between closely connected nodes. We also investigate the role of fixed connections which are not subject to these dynamics. We find that even a small number of fixed edges can significantly limit polarization, but still lead to multimodal opinion distributions, which may be considered polarized in a different sense.

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RePBubLik: Reducing Polarized Bubble Radius with Link Insertions

Cited by 24 publications

References 54 publications

Temporal Walk Centrality: Ranking Nodes in Evolving Networks

Temporal Walk Centrality: Ranking Nodes in Evolving Networks

Towards Consensus: Reducing Polarization by Perturbing Social Networks

Local Edge Dynamics and Opinion Polarization

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