Diffusion Maximization in Evolving Social Networks

Gayraud, Nathalie; Pitoura, Evaggelia; Tsaparas, Panayiotis

doi:10.1145/2817946.2817965

Cited by 28 publications

(25 citation statements)

References 23 publications

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“…Despite that the IC model on the dynamic network has been discovered, the network's dynamic concept is only exploited under the structure fluctuation while the probability of infection from this object to another is always fixed during the propagation process. Therefore, we propose the H‐IC spreading model on a dynamic agent's network , which is not only comprised of the network's topology fluctuation but also the transformation of agent's topic distribution (see Algorithm 3).…”

Section: Independent Cascade Model Based On Homophily (H‐ic)mentioning

confidence: 99%

Dynamic social network analysis: A novel approach using agent‐based model, author‐topic model, and pretopology

Bui

2019

Concurrency and Computation

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Summary We propose in this work a novel approach for dynamic social network analysis by combining an agent‐based model, an author‐topic model, and pretopology. We first introduce an analytical model for a dynamic social network associated with textual content using agent‐based and author‐topic models, namely, Textual‐ABM. The purpose of Textual‐ABM is to support for the concept exploitation of the “dynamics” of a social network, which contains not only network's structure transformation but also agent's interest variation over time. Agent's interest is revealed through topic probability distribution, which is estimated based on textual data using an author‐topic model. In addition to demonstrating the fluctuation of the social network related to textual content, we also exploit information propagation phenomena by proposing two expanded spreading models. The first model is an expanded model of an independent cascade model in which probability of infection is formed on homophily, namely H‐IC. We have implemented experiments on a collected dataset from the Neural Information Processing Systems Conference and have acquired satisfying results. Furthermore, we propose an extended model of pretopological cascade model from our previous work, namely, Textual‐PCM. The advantage of PCM comparison with classical cascade model is to utilize pseudoclosure function built from pretopology to define the more complex set of neighborhoods. In this work, we expand PCM to apply detail for a social network related to textual information. A toy example with some experiments and discussion is illustrated for Textual‐PCM. The work in this paper is an extended version of our paper dynamic social network analysis using author‐topic model presented in I4CS 2018 Conference.

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Section: Independent Cascade Model Based On Homophily (H‐ic)mentioning

confidence: 99%

Dynamic social network analysis: A novel approach using agent‐based model, author‐topic model, and pretopology

Bui

2019

Concurrency and Computation

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“…We use two different base TempInfMax methods: ForwardInfluence [2] for the SI model and Greedy-OT [7] for the PersistentIC model. As our approach is general (our results can be easily extended to other models), and our actual algorithm/output is modelindependent, we expect CondInf to perform well for both these methods.…”

Section: Algorithm 2 Condinfmentioning

confidence: 99%

“…the conditions under which a virus causes an epidemic [6,16,17]. Examples of propagation-based optimization problems are influence maximization [10,7,2], and immunization [26]. Remotely related work deals with weak and strong ties over time for diffusion [9].…”

Section: Related Workmentioning

confidence: 99%

Condensing Temporal Networks using Propagation

Adhikari¹,

Zhang²,

Bharadwaj³

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Modern networks are very large in size and also evolve with time. As their size grows, the complexity of performing network analysis grows as well. Getting a smaller representation of a temporal network with similar properties will help in various data mining tasks. In this paper, we study the novel problem of getting a smaller diffusion-equivalent representation of a set of time-evolving networks.We first formulate a well-founded and general temporalnetwork condensation problem based on the so-called system-matrix of the network. We then propose NetCondense, a scalable and effective algorithm which solves this problem using careful transformations in sub-quadratic running time, and linear space complexities. Our extensive experiments show that we can reduce the size of large real temporal networks (from multiple domains such as social, co-authorship and email) significantly without much loss of information. We also show the wide-applicability of NetCondense by leveraging it for several tasks: for example, we use it to understand, explore and visualize the original datasets and to also speed-up algorithms for the influencemaximization problem on temporal networks.

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“…Their work laid theoretical and algorithmic foundations for understanding influence diffusion and addressing the influencemaximization problem. Since then, there has been a large amount of follow-up work in two main directions: (1) extensive research has been done to study various extensions to the IC and LT models [8,13,19,44,49,55]; (2) a large number of algorithms have been proposed to improve efficiency in finding the K-seed set [22,51,64,81,125].…”

Section: Introductionmentioning

confidence: 99%

“…The LT model has also been extended to account for competition of influence diffusion in social networks [13,55]. Moreover, Gayraud et al [44] extend both IC and LT models to evolving social networks. These extensions are the substantial complement of the classic IC and LT models.…”

Section: Introductionmentioning

confidence: 99%

Modeling influence diffusion in networks for community detection, resilience analysis and viral marketing

Wang¹

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Responses of the four growth models to random disruptions, plotted as (a) supply availability rate, (b) size of LFSN, (c) average supply path length in LFSN, and (d) maximum supply path length in LFSN. .. .. .. .. 92 3.7 Responses of the four growth models to targeted attacks, plotted as (a) supply availability rate, (b) size of LFSN, (c) average supply path length in LFSN, and (d) maximum supply path length in LFSN. .. .. .. .. 94 3.8 Resilience scores of the four growth models to random disruptions. .. . 95 3.9 Resilience scores of the four growth models to targeted attacks, plotted as (a) 0-80 percent of nodes removed, and (b) 5-30 percent of nodes removed 96 3.10 Aggregated resilience scores of the four growth models under random disruptions and targeted attacks, plotted as (a) 0-80 percent of nodes removed, and (b) 5-30 percent of nodes removed .

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Diffusion Maximization in Evolving Social Networks

Cited by 28 publications

References 23 publications

Dynamic social network analysis: A novel approach using agent‐based model, author‐topic model, and pretopology

Dynamic social network analysis: A novel approach using agent‐based model, author‐topic model, and pretopology

Condensing Temporal Networks using Propagation

Modeling influence diffusion in networks for community detection, resilience analysis and viral marketing

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