Balance Correlations, Agentic Zeros, and Networks: The Structure of 192 Years of War and Peace

Original balance theory (Heider 1944) predicts human relations based on perceptions and attitudes between a pair of individuals (P - O) towards an inanimate object X. Social network extensions of his theory have replaced this X with a third individual. This has led to a plethora of adaptations that have often been inconsistent with Heider and with each other. We present a general model and formal notation for these social network extensions that permit social scientists to be more explicit about their balance theoretic statements. Specifically, we formulate statements as a comparison of two conditional probabilities of a tie, where the conditionals are defined by the 2-path relation Ego - X - Alter. Given the importance Heider assigns to the role of negative associations, we further identify negative ties as separate from non-ties (neutral or zero-valued ties) and consider a signed graph to be a restricted multigraph composed of three mutually exclusive and exhaustive relations: positive ties, negative ties, and zero-ties. We stress that neutrality is the result of a triadic process. Combining these two features into our theoretical frame results in 27 identifiable configurations. Drawing on the work on Transitivity Correlation models, we propose a set of simple descriptive statistics to measure the extent to which evidence for any stipulated balance configuration is present in a network. Finally, we demonstrate how to apply this approach to assess network-level balance in a large data set consisting of friendly vs hostile relations between countries from 1816 to 2007. We find strong evidence particularly for one of the four classic Heiderian balance theory predictions, and virtually no evidence in support of the imbalance predictions. However, we do find stable and surprising evidence that `neutral' ties are important in balancing the relations among nations.Comment: 31 pages, presented at Networks 2021, Bloomington, USA, Sunbelt 2022, Cairns, Sunbelt 2023, Portland, Sunbelt 2023, Portland, USA, ION IX, Lexington, USA, and EUSN 2023 Ljubljana, Sloveni

Contrasting Multiple Social Network Autocorrelations for Binary Outcomes, With Applications To Technology Adoption

The rise of socially targeted marketing suggests that decisions made by consumers can be predicted not only from their personal tastes and characteristics, but also from the decisions of people who are close to them in their networks. One obstacle to consider is that there may be several different measures for "closeness" that are appropriate, either through different types of friendships, or different functions of distance on one kind of friendship, where only a subset of these networks may actually be relevant. Another is that these decisions are often binary and more difficult to model with conventional approaches, both conceptually and computationally. To address these issues, we present a hierarchical model for individual binary outcomes that uses and extends the machinery of the auto-probit method for binary data. We demonstrate the behavior of the parameters estimated by the multiple network-regime auto-probit model (m-NAP) under various sensitivity conditions, such as the impact of the prior distribution and the nature of the structure of the network, and demonstrate on several examples of correlated binary data in networks of interest to Information Systems, including the adoption of Caller Ring-Back Tones, whose use is governed by direct connection but explained by additional network topologies

Transitivity correlation:A descriptive measure of network transitivity

This paper proposes that common measures for network transitivity, based on the enumeration of transitive triples, do not reflect the theoretical statements about transitivity they aim to describe. These statements are often formulated as comparative conditional probabilities, but these are not directly reflected by simple functions of enumerations. We think that a better approach is obtained by considering the probability of a tie between two randomly drawn nodes, conditional on selected features of the network. Two measures of transitivity based on correlation coefficients between the existence of a tie and the existence, or the number, of two-paths between the nodes are developed, and called "Transitivity Phi" and "Transitivity Correlation." Some desirable properties for these measures are studied and compared to existing clustering coefficients, in both random (Erdos-Renyi) and in stylized networks (windmills). Furthermore, it is shown that in a directed graph, under the condition of zero Transitivity Correlation, the total number of transitive triples is determined by four underlying features: size, density, reciprocity, and the covariance between in- and outdegrees. Also, it is demonstrated that plotting conditional probability of ties, given the number of two-paths, provides valuable insights into empirical regularities and irregularities of transitivity patterns

Dynamic Effects of Trust and Cognitive Social Structures on Information Transfer Relationships

Changes in relationships are due to human actions. We assume that these human actions are functions of perceptions of a focal individual, but also the perceptions of other individuals who are part of the organizational and social environment. We hypothesize that perceptions based trust and perceptions of the structural environment individuals operate in affect relationship change more than the "actual" environment in which individuals operate. An empirically analysis shows the dynamic effects of perceptions on changes in two types of relationships, which are believed to be important in account management. We explore, 1, whether the levels of perceptions, and, 2, whether changes in perceptions affect relationship changes. For example, we consider the effects of the amount of trust as well as the change in the amount of trust one individual puts in another individual. We find that perceptions have more impact on relationship change than "actual" network variables have. Furthermore, the results show that it is useful to distinguish between level and change effects of perceptions

An Equilibrium-Correction Model for Dynamic Network Data

We propose a two-stage MRQAP to analyze dynamic network data, within the framework of an equilibrium-correction (EC) model. Extensive simulation results indicate practical relevance of our method and its improvement over standard OLS. An empirical illustration additionally shows that the EC model yields interpretable parameters, in contrast to an unrestricted dynamic model

Peer influence in the diffusion of iPhone 3G over a large social network

In this paper, we study the effect of peer influence in the diffusion of the iPhone 3G across a number of communities sampled from a large dataset provided by a major European Mobile carrier in one country. We identify tight communities of users in which peer influence may play a role and use instrumental variables to control for potential correlation between unobserved subscriber heterogeneity and friends' adoption. We provide evidence that the propensity of a subscriber to adopt increases with the percentage of friends who have already adopted. During a period of 11 months, we estimate that 14 percent of iPhone 3Gs sold by this carrier were due to peer influence. This result is obtained after controlling for social clustering, gender, previous adoption of mobile Internet data plans, ownership of technologically advanced handsets, and heterogeneity in the regions where subscribers move during the day and spend most of their evenings. This result remains qualitatively unchanged when we control for changes over time in the structure of the social network. We provide results from several policy experiments showing that, with this level of effect of peer influence, the carrier would have hardly benefitted from using traditional marketing strategies to seed the iPhone 3G to benefit from viral marketing.info:eu-repo/semantics/publishedVersio

An equilibrium-correction model for dynamic network data

We propose a two-stage MRQAP to analyze dynamic network data, within the framework of an equilibrium-correction (EC) model. Extensive simulation results indicate practical relevance of our method and its improvement over standard OLS. An empirical illustration additionally shows that the EC model yields interpretable parameters, in contrast to an unrestricted dynamic model

Transferring collective knowledge: teaching and learning in the Chinese auto industry

U.S. for their assistance and insights. We are grateful for thoughtful suggests from Kath

Social capital and the Creation of Knowledge

This paper examines the relationship between the social capital and knowledge creation in research, mostly in the context of universities. The analysis is developed considering all of the following critical aspects of social capital: direct ties, strengths of direct ties, density, structural holes, centrality, and external-internal index in terms of fields of knowledge. Two important results arise from this research. First, the overall results suggest that, when controlling for other network variables and individual heterogeneity, the effects of the structural holes variable disappear. This result stands in contrast to the established idea that structural holes is the most important variable to represent social capital and, therefore, is seen as contributing to superior performance. Second, the results show that with this strong set of controls, what matters in social capital is having many direct ties, being in a central position, having partners from different areas of knowledge, and being part of a non dense network