Inferring Dynamic Diffusion Networks in Online Media

Abstract

Online media play an important role in information societies by providing a convenient infrastructure for different processes. Information diffusion that is a fundamental process taking place on social and information networks has been investigated in many studies. Research on information diffusion in these networks faces two main challenges: (1) In most cases, diffusion takes place on an underlying network, which is latent and its structure is unknown. (2) This latent network is not fixed and changes over time. In this article, we investigate the diffusion network extraction (DNE) problem when the underlying network is dynamic and latent. We model the diffusion behavior (existence probability) of each edge as a stochastic process and utilize the Hidden Markov Model (HMM) to discover the most probable diffusion links according to the current observation of the diffusion process, which is the infection time of nodes and the past diffusion behavior of links. We evaluate the performance of our Dynamic Diffusion Network Extraction (DDNE) method, on both synthetic and real datasets. Experimental results show that the performance of the proposed method is independent of the cascade transmission model and outperforms the state of art method in terms of F-measure.

Recommender System-Based Diffusion Inferring for Open Social Networks

“…Cascade based methods assume the underlying network to be static, but the network situation is usually dynamic in reality. To cope with this shortcoming, [24] proposes an algorithm that constructs information diffusion network while the diffusion process is both dynamic and potential. Proposed method in [24] models the existence of all edges as a stochastic propagation process and use hidden Markov model to reproduce diffusion process.…”

“…To cope with this shortcoming, [24] proposes an algorithm that constructs information diffusion network while the diffusion process is both dynamic and potential. Proposed method in [24] models the existence of all edges as a stochastic propagation process and use hidden Markov model to reproduce diffusion process. [25] supposes that users' interactions show various features and propagation speed, and construct a mixed diffusion pattern model to infer the information network of heterogeneity.…”

Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization

“…Structure inference: When the interaction topology of a network is entirely unreachable, an inference approach is put forward using visible independent measurements [14], utilizing the process over the network or with the help of received signals from nodes to infer their connections. Many attempts have been made to solve this problem only based on diffusion knowledge, from static assumption requiring time stamp [15], [16], [17], [18] or without infection time [19] to dynamic inference [20], [21]. As a step further towards using prior knowledge, some works employ in-degree distribution for nodes [22], and measurements such as pathways, network properties, and information about the links or nodes [23].…”

Tensor Decompositions for Identifying Directed Graph Topologies and Tracking Dynamic Networks

“…Dynamic SEMs were also advocated for inference of dynamic and directed cascade networks in [3]. More recent work in [34] resorted to hidden Markov models (HMMs) to track diffusion links.…”

“…Dynamic SEMs were also advocated for inference of dynamic and directed cascade networks in [3]. More recent work in [34] resorted to hidden Markov models (HMMs) to track diffusion links. PARAFAC decompositions have previously been advocated in e.g., blind source separation (BSS) tasks, which separate source signals from their mixed observations; see e.g., [18], [22].…”