Diffusion Centrality Research Articles

Diffusion and targeting centrality

This paper studies the dynamics of information diffusion within networks, encompassing both general and targeted dissemination. We first characterize the theoretical foundations of diffusion centrality. Next, we introduce two extensions of diffusion centrality: targeting centrality and reachability, that we believe to better capture situations involving targeted requests. We derive general explicit formulas for the computation of these novel centrality measures.

Identifying the key player in the diffusion network of global economic policy uncertainty

Within a generalized vector autoregressive framework comprising 22 country-level economic policy uncertainty indices, this study used a 12-period-ahead forecast error variance decomposition to compute spillovers. The results showed that although it has been in a downtrend since the third quarter of 2016, the overall spillover among the major economies remains high. Net spillover analyses in either the full or rolling samples illustrated that Singapore was the most decisive transmitter of spillovers, followed by Japan. Meanwhile, Ireland, the Netherlands, Russia, Brazil, Colombia, Italy, and Mexico were clear receivers of net spillover. After reorganizing the net pairwise spillovers into a directed weighted network, the core–periphery structure and backbone were extracted, and the diffusion centrality was calculated to ascertain the key player. Regardless of using the full or rolling samples, Singapore played an influential role in the diffusion network of global economic policy uncertainty.

Research on urban economic centrality in the perspectives of knowledge stocks and flows

In light of the profound advancements in information and communication technologies ushering in the knowledge economy, the urban space is undergoing a transformation from the traditional “space of places” to the emerging “space of flows.” This shift poses questions about the influence of knowledge flows on urban economic centrality. This paper seeks to address this knowledge gap by introducing a theoretical framework that elucidates how knowledge contributes to urban economic centrality. Our analysis focuses on both intra-city knowledge stocks and inter-city knowledge flows. Empirical findings in China highlight that knowledge stocks based on R&D and education levels, along with knowledge flows, significantly and positively influence all four dimensions of centrality. In contrast, knowledge spatial agglomeration within the knowledge stocks exhibits a significant positive correlation solely with power centrality. Additionally, although the knowledge stock structures don't yield significant results in the diffusion centrality model, for agglomeration and power centralities, a combination of specialized knowledge and telecommunications enhances urban economic centrality, while face-to-face communication strengthens the positive impact of diverse knowledge on urban economic centrality. The results suggest implementing knowledge-based policies tailored to different nodes in the city network is expected to promote sustainable competitiveness in the urban system.

The Diffusion of Smoking: Association Between School Tobacco Policies and the Diffusion of Adolescent Smoking in 38 Schools in 6 Countries.

Social network research has evidenced the role of peer effects in the adoption of behaviours. Little is known, however, about whether policies affect how behaviours are shared in a network. To contribute to this literature, we apply the concept of diffusion centrality to school tobacco policies and adolescent smoking. Diffusion centrality is a measure of centrality which refers to a person's ability to diffuse a given property-in our case, smoking-related behaviours. We hypothesized that stronger school tobacco policies are associated with less diffusion centrality of smoking on school premises and of smoking in general. A whole network study was carried out in 2013 and 2016 among adolescents (n = 18,805) in 38 schools located in six European cities. Overall, diffusion centrality of smoking in general and of smoking on school premises significantly decreased over time. Diffusion centrality of smoking significantly decreased both in schools where the policy strengthened or softened over time, but for diffusion of smoking on school premises, this decrease was only significant in schools where it strengthened. Finally, stronger school tobacco policies were associated with lower diffusion centrality of smoking on school premises and of smoking in general, though to a lesser extent. With such policies, smoking may, therefore, become less prevalent, less popular, and less clustered, thereby lowering the risk of it spreading within networks in, and even outside the school.

Diffusion centrality: A paradigm to maximize spread in social networks

We propose Diffusion Centrality (DC) in which semantic aspects of a social network are used to characterize vertices that are influential in diffusing a property p. In contrast to classical centrality measures, diffusion centrality of vertices varies with the property p, and depends on the diffusion model describing how p spreads. We show that DC applies to most known diffusion models including tipping, cascade, and homophilic models. We present a hypergraph-based algorithm (HyperDC) with many optimizations to exactly compute DC. However, HyperDC does not scale well to huge social networks (millions of vertices, tens of millions of edges). For scaling, we develop methods to coarsen a network and propose a heuristic algorithm called “Coarsened Back and Forth” (CBAF) to compute the top-k vertices (having the highest diffusion centrality). We report on experiments comparing DC with classical centrality measures in terms of runtime and the “spread” achieved by the k most central vertices (using 7 real-world social networks and 3 different diffusion models). Our experiments show that DC produces higher quality results and is comparable to several centrality measures in terms of runtime.

Compliance Behavior in Networks: Evidence from a Field Experiment

This paper studies the spread of compliance behavior in neighborhood networks in Austria. We exploit a field experiment that varied the content of mailings sent to potential evaders of TV license fees. The data reveal a strong treatment spillover: untreated households are more likely to switch from evasion to compliance in response to mailings received by their network neighbors. Digging deeper into the properties of the spillover, we find that it is concentrated among close neighbors of the targets and increases with the treated households’ diffusion centrality. Local concentration of equally treated households implies a lower spillover. (JEL C93, D12, D85, L82, L88, Z13)

Parallelization of game theoretic centrality algorithms

Communication has become a lot easier with the advent of easy and cheap means of reaching people across the globe. This has allowed the development of large networked communities and, with the technology available to track them, has opened up the study of social networks at unprecedented scales. This has necessitated the scaling up of various network analysis algorithms that have been proposed earlier in the literature. While some algorithms can be readily adapted to large networks, in many cases the adaptation is not trivial. In this work, we explore the scaling up of a class of node centrality algorithms based on cooperative game theory. These were proposed earlier as an efficient alternatives to traditional measure of information diffusion centrality. We present here distributed versions of these algorithms in a Map-Reduce framework, currently the most popular distributed computing paradigm. We empirically demonstrate the scaling behavior of our algorithm on very large synthetic networks thereby establishing the utility of these methods in settings such as online social networks.

Gossip: Identifying Central Individuals in a Social Network

Is it possible, simply by asking a few members of a community, to identify individuals who are best placed to diffuse information? A simple model of shows how boundedly rational individuals can, just by tracking gossip about people, identify those who are most central in a network according to diffusion (a measure of network centrality which nests existing ones, and predicts the extent to which piece of information seeded to a network member diffuses in finite time). Using rich network data from 35 Indian villages, we find that respondents accurately nominate those who are central -- not just traditional leaders or those with many friends. In a subsequent randomized field experiment in 213 villages, we track the of a piece of information initially given to a small number of in each community. Seeds who are nominated by others lead to a near tripling of the spread of information relative to randomly chosen seeds. Diffusion centrality accounts for some, but not all, of the extra from these nominated seeds compared to other seeds (including those with high social status) in our experiment.

The Diffusion of Microfinance

To study the impact of the choice of injection points in the diffusion of a new product in a society, we developed a model of word-of-mouth diffusion and then applied it to data on social networks and participation in a newly available microfinance loan program in 43 Indian villages. Our model allows us to distinguish information passing among neighbors from direct influence of neighbors' participation decisions, as well as information passing by participants versus nonparticipants. The model estimates suggest that participants are seven times as likely to pass information compared to informed nonparticipants, but information passed by nonparticipants still accounts for roughly one-third of eventual participation. An informed household is not more likely to participate if its informed friends participate. We then propose two new measures of how effective a given household would be as an injection point. We show that the centrality of the injection points according to these measures constitutes a strong and significant predictor of eventual village-level participation.