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The weakness of weak ties for novel information diffusion.

ABSTRACT: Weak ties are thought to facilitate the diffusion of information through social networks because of their tendency to span otherwise distant subgroups. However, this logic assumes that weak relationships have the same capacity to transmit information as those that are strong. I argue that weak ties, especially the kind that span subgroups, are often also lower-capacity. Due to a lack of trust, an unwillingness to share benefits, or a limited ability to understand one another, an individual is less likely to share novel information across these ties. In standard models of diffusion imported from epidemiology, even reduced-capacity links would still aid diffusion. However, accounting for reduced capacity in a new model of diffusion that captures realistic features of information sharing in human groups, I demonstrate that hesitation to share across weak links substantially impedes overall diffusion. Moreover, I show that the addition of weak ties to a social network can strictly reduce the extent and speed of information diffusion. Increasing density by adding weak ties can make diffusion strictly worse by crowding out the use of higher-capacity ties. I present the results of simulated information diffusion on both hypothetical networks generated to possess varying levels of density and homophily, as well as on real social networks in two Ugandan villages shown to be responsible for face-to-face information sharing.

SUBMITTER: Larson JM

PROVIDER: S-EPMC6214243 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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The weakness of weak ties for novel information diffusion.

Larson Jennifer M JM

Applied network science 20170619 1

Weak ties are thought to facilitate the diffusion of information through social networks because of their tendency to span otherwise distant subgroups. However, this logic assumes that weak relationships have the same capacity to transmit information as those that are strong. I argue that weak ties, especially the kind that span subgroups, are often also lower-capacity. Due to a lack of trust, an unwillingness to share benefits, or a limited ability to understand one another, an individual is le ...[more]

PMID: 30443569

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Project description:Progress has been made in understanding how temporal network features affect the percentage of nodes reached by an information diffusion process. In this work, we explore further: which node pairs are likely to contribute to the actual diffusion of information, i.e., appear in a diffusion trajectory? How is this likelihood related to the local temporal connection features of the node pair? Such deep understanding of the role of node pairs is crucial to tackle challenging optimization problems such as which kind of node pairs or temporal contacts should be stimulated in order to maximize the prevalence of information spreading. We start by using Susceptible-Infected (SI) model, in which an infected (information possessing) node could spread the information to a susceptible node with a given infection probability ? whenever a contact happens between the two nodes, as the information diffusion process. We consider a large number of real-world temporal networks. First, we propose the construction of an information diffusion backbone GB(?) for a SI spreading process with an infection probability ? on a temporal network. The backbone is a weighted network where the weight of each node pair indicates how likely the node pair appears in a diffusion trajectory starting from an arbitrary node. Second, we investigate the relation between the backbones with different infection probabilities on a temporal network. We find that the backbone topology obtained for low and high infection probabilities approach the backbone GB(????0) and GB(??=?1), respectively. The backbone GB(????0) equals the integrated weighted network, where the weight of a node pair counts the total number of contacts in between. Finally, we explore node pairs with what local connection features tend to appear in GB(??=?1), thus actually contribute to the global information diffusion. We discover that a local connection feature among many other features we proposed, could well identify the (high-weight) links in GB(??=?1). This local feature encodes the time that each contact occurs, pointing out the importance of temporal features in determining the role of node pairs in a dynamic process.

Dataset Information

The weakness of weak ties for novel information diffusion.

Publications

The weakness of weak ties for novel information diffusion.

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