An Information Flow Model for Conflict and Fission in Small Groups

By Konstantin Avrachenkov, V. Mazalov. 1977

Data from a voluntary association are used to construct a new formal model for a traditional anthropological problem, fission in small groups. The process leading to fission is viewed as an unequal flow of sentiments and information across the ties in a social network. This flow is unequal because it is uniquely constrained by the contextual range and sensitivity of each relationship in the network. The subsequent differential sharing of sentiments leads to the formation of subgroups with more internal stability than the group as a whole, and results in fission. The Ford-Fulkerson labeling algorithm allows an accurate prediction of membership in the subgroups and of the locus of the fission to be made from measurements of the potential for information flow across each edge in the network. Methods for measurement of potential information flow are discussed, and it is shown that all appropriate techniques will generate the same predictions.

Subfields: Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Social Sciences & Humanities

Citations in the corpus

Semi-Supervised Classification with Graph Convolutional Networks (2016, methodology): “Lastly, we compare against Planetoid (Yang et al., 2016), where we always choose their best-performing model variant (transductive vs. inductive) as a baseline.”
DeepWalk: online learning of social representations (2014, methodology): “Here, our method is used on Zachary’s Karate network [44] to generate a latent representation in R.”
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning (2018, methodology): “We apply GCNs with different number of layers on the Zachary’s karate club dataset (Zachary 1977), which has 34 vertices of two classes and 78 edges.”
Community detection in graphs (2009, methodology): “The algorithm scales in a time O(mn), or O(n2) on sparse graphs, and gives good results in practical examples, including Zachary’s karate club [50] and the benchmark by Girvan and Newman [12] (Section 15.”
Graph Embedding Techniques, Applications, and Performance: A Survey (2017, background): “KARATE [60]: Zachary’s karate network is a well-known social network of a university karate club.”
Modularity and community structure in networks. (2006, background): “…of vertices (shaded) within which there are many edges, with only a smaller number of edges between vertices of different groups. www.pnas.org cgi doi 10.1073 pnas.0601602103 PNAS June 6, 2006 vol. 103 no. 23 8577–8582 A PP LI ED M A TH EM A TI CS D ow nl oa de d by g ue st o n Ja nu ar y 3, 2 02 1”
Finding and evaluating community structure in networks. (2003, background): “8: The network of friendships between individuals in the karate club study of Zachary [35].”
Community Structure in Graphs (2007, background): “During two years, the sociologist Wayne Zachary observed the ties between members, both inside and outside the club [95].”
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks (2018, methodology): “Here, we show the encoding capability of clique embedding in NetWalk by applying our method to the Zachary’s karate network [42].”
On Modularity Clustering (2008, methodology): “The first instance is the karate club network of Zachary originally introduced in [25] and used for demonstration in [26].”
The Ubiquity of Small-World Networks (2011, methodology): “The karate(Zachary 1977), word adjacency(Newman 2006), football(Girvan and Newman 2002), dolphin(Lusseau et al.”
Manufacturing Consent (2015, background): “1) Zachary Karate Club: This is a well known network representing friendships between 34 members of a karate club over a period of two years [31].”
Modularity-maximizing graph communities via mathematical programming (2007, background): “The “Zachary’s Karate Club” [38] network represents the friendships between 34 members of a karate club in the US over a period of 2 years.”
Graph Representation Learning (2020, methodology): “During Zachary’s study, the club split into two factions—centered around nodes 0 and 33—and Zachary was able to correctly predict which nodes would fall into each faction based on the graph structure [Zachary, 1977].”
Adversarial Network Embedding (2017, background): “Figure 1: Visualization of two dimensional representations of Zachary’s Karate network (Zachary 1977) from Inductive DeepWalk (IDW) and Adversarial Inductive DeepWalk (AIDW).”