Coevolutionary opinion formation games

Bhawalkar, Kshipra; Gollapudi, Sreenivas; Munagala, Kamesh

doi:10.1145/2488608.2488615

Cited by 59 publications

(66 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Das, Gollapudi, Munagala use a model which combines the continuous and discrete approach to fit opinion data better [15]. Bhawalkar, Gollapudi, Munagala study the scenario where the network and the agents' opinions co-evolve [4].…”

Section: Related Workmentioning

confidence: 99%

Minimizing Polarization and Disagreement in Social Networks

Musco

Tsourakakis

2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

108

131

View full text Add to dashboard Cite

The rise of social media and online social networks has been a disruptive force in society. Opinions are increasingly shaped by interactions on online social media, and social phenomena including disagreement and polarization are now tightly woven into everyday life. In this work we initiate the study of the following question:Given n agents, each with its own initial opinion that reflects its core value on a topic, and an opinion dynamics model, what is the structure of a social network that minimizes polarization and disagreement simultaneously?This question is central to recommender systems: should a recommender system prefer a link suggestion between two online users with similar mindsets in order to keep disagreement low, or between two users with different opinions in order to expose each to the other's viewpoint of the world, and decrease overall levels of polarization? Such decisions have an important global effect on society [51]. Our contributions include a mathematical formalization of this question as an optimization problem and an exact, time-efficient algorithm. We also prove that there always exists a network with O(n/ 2 ) edges that is a (1 + ) approximation to the optimum. Our formulation is an instance of optimization over graph topologies, also considered e.g., in [7,12,48]. For a fixed graph, we additionally show how to optimize our objective function over the agents' innate opinions in polynomial time.We perform an empirical study of our proposed methods on synthetic and real-world data that verify their value as mining tools to better understand the trade-off between of disagreement and polarization. We find that there is a lot of space to reduce both polarization and disagreement in real-world networks; for instance, on a Reddit network where users exchange comments on politics, our methods achieve a ∼ 60 000-fold reduction in polarization and disagreement. Our code is available at https://github.com/tsourolampis/polarization-disagreement.

show abstract

Section: Related Workmentioning

confidence: 99%

Minimizing Polarization and Disagreement in Social Networks

Musco

Tsourakakis

2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

108

131

View full text Add to dashboard Cite

show abstract

“…Thus, we are interested to bound the price of anarchy for games with directed graphs more general than weighted Eulerian graphs (even with just quadratic individual cost functions) in this article. Note that although the result of [2] is indeed for directed graphs and gives bounded price of anarchy, their setting is different from ours. In their model, the weights on the k neighbors are uniform, and more importantly, the k neighbors of a node is not fixed, as its action in the game includes choosing its k neighbors in addition to its opinion.…”

Section: Introductionmentioning

confidence: 73%

“…Nevertheless, a bounded price of anarchy can be obtained for weighted Eulerian graphs, in particular, a tight upper bound of 2 for directed cycles and an upper bound of d + 1 for d-regular graphs. Another work closely related to that of Bindel et al is by Bhawalkar et al [2]. The individual cost functions are assumed to be "locally-smooth" in the sense of [12] and may be more general than quadratic, for example, convex.…”

Section: Introductionmentioning

confidence: 99%

Bounds on the price of anarchy for a more general class of directed graphs in opinion formation games

Chen

2016

Operations Research Letters

View full text Add to dashboard Cite

In opinion formation games with directed graphs, a bounded price of anarchy is only known for weighted Eulerian graphs. Thus, we bound the price of anarchy for a more general class of directed graphs with conditions intuitively meaning that each node does not influence the others more than she is influenced, where the bounds depend on such difference (in a ratio). We also show that there exists an example just slightly violating the conditions with an unbounded price of anarchy.

show abstract

“…Since in many real-world applications there is no consensus and the opinions are usually fragmented into several parts, we use a variant of the Degroot model, which is introduced by Friedkin and Johnsen [4], in which consensus is not necessarily reached. This model recently has become popular in the computer science literature (see e.g., [1,2,5]). In this model, each node has an inherent internal opinion si which remains unchanged during the process and an expressed overall opinion zi which is dynamically updated through weighted averaging of the node's internal opinion and its neighbors' expressed opinions.…”

Section: Introductionmentioning

confidence: 99%

How effectively can we form opinions?

Ahmadinejad

Dehghani

Hajiaghayi

et al. 2014

Proceedings of the 23rd International Conference on World Wide Web

View full text Add to dashboard Cite

People make decisions and express their opinions according to their communities. An appropriate idea for controlling the diffusion of an opinion is to find influential people, and employ them to spread the desired opinion. We investigate an influencing problem when individuals' opinions are affected by their friends due to the model of Friedkin and Johnsen [4]. Our goal is to design efficient algorithms for finding opinion leaders such that changing their opinions has great impact on the overall opinion of the society. We define a set of problems like maximizing the sum of individual opinions or maximizing the number of individuals whose opinions are above a threshold. We discuss the complexity of the defined problems and design optimum algorithms for the non NP-hard variants of the problems. Furthermore, we run simulations on real-world social network data and show our proposed algorithm outperforms the classical algorithms such as degree-based, closeness-based, and pagerank-based algorithms.

show abstract

Coevolutionary opinion formation games

Cited by 59 publications

References 17 publications

Minimizing Polarization and Disagreement in Social Networks

Minimizing Polarization and Disagreement in Social Networks

Bounds on the price of anarchy for a more general class of directed graphs in opinion formation games

How effectively can we form opinions?

Contact Info

Product

Resources

About