A Survey on Information Diffusion Models in Social Networks

Singh, Shashank Sheshar; Singh, Kuldeep; Kumar, Ajay; Shakya, Harish Kumar

doi:10.1007/978-981-13-3143-5_35

Cited by 26 publications

(23 citation statements)

References 18 publications

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“…Knowledge and behavior flow within social interactions which results in the adoption of innovations, endorsement of opinions, and spread of ideas, just to mention a few examples (Guilbeault et al, 2018) (Anderson et al, 2001;Fields & Kafai, 2009). These phenomena spread within the fabric of social networks through the process of diffusion (Anderson et al, 2001;Singh, 2018). Relationships between individuals are the joints in pathways through which information flows.…”

Section: Introductionmentioning

confidence: 99%

Modelling diffusion in computer-supported collaborative learning: a large scale learning analytics study

Saqr

López-Pernas

2021

Intern. J. Comput.-Support. Collab. Learn

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This study empirically investigates diffusion-based centralities as depictions of student role-based behavior in information exchange, uptake and argumentation, and as consistent indicators of student success in computer-supported collaborative learning. The analysis is based on a large dataset of 69 courses (n = 3,277 students) with 97,173 total interactions (of which 8,818 were manually coded). We examined the relationship between students’ diffusion-based centralities and a coded representation of their interactions in order to investigate the extent to which diffusion-based centralities are able to adequately capture information exchange and uptake processes. We performed a meta-analysis to pool the correlation coefficients between centralities and measures of academic achievement across all courses while considering the sample size of each course. Lastly, from a cluster analysis using students’ diffusion-based centralities aimed at discovering student role-taking within interactions, we investigated the validity of the discovered roles using the coded data. There was a statistically significant positive correlation that ranged from moderate to strong between diffusion-based centralities and the frequency of information sharing and argumentation utterances, confirming that diffusion-based centralities capture important aspects of information exchange and uptake. The results of the meta-analysis showed that diffusion-based centralities had the highest and most consistent combined correlation coefficients with academic achievement as well as the highest predictive intervals, thus demonstrating their advantage over traditional centrality measures. Characterizations of student roles based on diffusion centralities were validated using qualitative methods and were found to meaningfully relate to academic performance. Diffusion-based centralities are feasible to calculate, implement and interpret, while offering a viable solution that can be deployed at any scale to monitor students’ productive discussions and academic success.

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Section: Introductionmentioning

confidence: 99%

Modelling diffusion in computer-supported collaborative learning: a large scale learning analytics study

Saqr

López-Pernas

2021

Intern. J. Comput.-Support. Collab. Learn

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“…A variant of firefighter problem where b ≥ 2 vertices can be defended at each step has been shown not to be approximable within a constant factor [3]. There are many information diffusion models and broadcast scheduling methods in the literature [5,19,23], but the k-burning process seems to differ in the situation that at each step it allows k new sources to appear anywhere in the graph, i.e., some new burn locations may not be in close proximity of the currently burnt vertices.…”

Section: Related Workmentioning

confidence: 99%

APX-Hardness and Approximation for the k-Burning Number Problem

Mondal

Parthiban

Kavitha

et al. 2021

WALCOM: Algorithms and Computation

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Consider an information diffusion process on a graph G that starts with k > 0 burnt vertices, and at each subsequent step, burns the neighbors of the currently burnt vertices, as well as k other unburnt vertices. The k-burning number of G is the minimum number of steps b k (G) such that all the vertices can be burned within b k (G) steps. Note that the last step may have smaller than k unburnt vertices available, where all of them are burned. The 1-burning number coincides with the well-known burning number problem, which was proposed to model the spread of social contagion. The generalization to k-burning number allows us to examine different worst-case contagion scenarios by varying the spread factor k.In this paper we prove that computing k-burning number is APXhard, for any fixed constant k. We then give an O((n + m) log n)-time 3-approximation algorithm for computing k-burning number, for any k ≥ 1, where n and m are the number of vertices and edges, respectively. Finally, we show that even if the burning sources are given as an input, computing a burning sequence itself is an NP-hard problem.

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“…Recently, numerous studies have modeled information spread by analyzing the diffusion paths of information and using the structure of a social network to predict the next node that will spread the information [212]. Many of these analyses have used neural networks [88,213,214].…”

Section: Structural Information Spread Modelsmentioning

confidence: 99%

Role-Aware Information Spread in Online Social Networks

Bartal

Jagodnik

2021

Entropy

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Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information. However, they neglected to analyze non-viral information with low reach size that can also spread globally beyond OSN edges (links) via non-neighbors through, for example, pushed information via content recommendation algorithms. Previous reviews have also not fully considered user roles in the spread of information. To address these gaps, we: (i) provide a comprehensive survey of the latest studies on role-aware information spread in OSNs, also addressing the different temporal spreading patterns of viral and non-viral information; (ii) survey modeling approaches that consider structural, non-structural, and hybrid features, and provide a taxonomy of these approaches; (iii) review software platforms for the analysis and visualization of role-aware information spread in OSNs; and (iv) describe how information spread models enable useful applications in OSNs such as detecting influential users. We conclude by highlighting future research directions for studying information spread in OSNs, accounting for dynamic user roles.

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A Survey on Information Diffusion Models in Social Networks

Cited by 26 publications

References 18 publications

Modelling diffusion in computer-supported collaborative learning: a large scale learning analytics study

Modelling diffusion in computer-supported collaborative learning: a large scale learning analytics study

APX-Hardness and Approximation for the k-Burning Number Problem

Role-Aware Information Spread in Online Social Networks

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