DNE: A Method for Extracting Cascaded Diffusion Networks from Social Networks

Eslami, Motahhare; Rabiee, Hamid R.; Salehi, Mostafa

doi:10.1109/passat/socialcom.2011.85

Cited by 17 publications

(29 citation statements)

References 22 publications

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“…The reconstruction is done through the edges of the network, while the nodes are known in advance. Eslami et al [Eslami et al 2011] studied the same problem. They modeled the diffusion process as a Markov random walk and proposed an algorithm called DNE to discover the most probable diffusion links.…”

Section: Related Workmentioning

confidence: 97%

“…One important factor that is often studied is the structure of these networks [Clauset et al 2008;Eslami et al 2011;Fortunato 2010;Freno et al ;Gomez-Rodriguez et al 2012;Gong et al 2011;Kim and Leskovec 2011;Leroy et al 2010;Liben-Nowell and Kleinberg 2007;Lin et al 2012;Porter et al 2009;Sadikov et al 2011]. Previously, a Link Prediction Problem [Clauset et al 2008;Liben-Nowell and Kleinberg 2007] was defined as attempting to locate which connections (edges) will soon exist between nodes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting and Identifying Missing Node Information in Social Networks

Eyal

Rosenfeld

Sina

et al. 2013

ACM Trans. Knowl. Discov. Data

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In recent years, social networks have surged in popularity. One key aspect of social network research is identifying important missing information which is not explicitly represented in the network, or is not visible to all. To date, this line of research typically focused on finding the connections that are missing between nodes, a challenge typically termed as the Link Prediction Problem. This paper introduces the Missing Node Identification problem where missing members in the social network structure must be identified. In this problem, indications of missing nodes are assumed to exist. Given these indications and a partial network, we must assess which indications originate from the same missing node and determine the full network structure.Towards solving this problem, we present the MISC Algorithm (Missing node Identification by Spectral Clustering), an approach based on a spectral clustering algorithm, combined with nodes' pairwise affinity measures which were adopted from link prediction research. We evaluate the performance of our approach in different problem settings and scenarios, using real life data from Facebook. The results show that our approach has beneficial results and can be effective in solving the Missing Node Identification Problem. In addition, this paper also presents R-MISC which uses a sparse matrix representation, efficient algorithms for calculating the nodes' pairwise affinity and a proprietary dimension reduction technique, to enable scaling the MISC algorithm to large networks of more than 100,000 nodes. Last, we consider problem settings where some of the indications are unknown. Two algorithms are suggested for this problem -Speculative MISC, based on MISC, and Missing Link Completion, based on classical link prediction literature. We show that Speculative MISC outperforms Missing Link Completion.

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Section: Related Workmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Predicting and Identifying Missing Node Information in Social Networks

Eyal

Rosenfeld

Sina

et al. 2013

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

show abstract

“…One potential limitation of the algorithms we introduce is the large sizes of their affinity matrices, something that 3 The purity measure is not applicable for the KronEM algorithm as it is not a clustering-based algorithm. may prevent these algorithms from scaling to networks with larger numbers of nodes.…”

Section: B Generalized Configurationmentioning

confidence: 99%

“…Scientists in both academia and industry have recognized the importance of these networks and have focused on various aspects of social networks. One aspect that is often studied is the structure of these networks [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]. Previously, a missing link problem [1], [2] was defined as attempting to locate which connections (edges) will soon exist between nodes.…”

Section: Introductionmentioning

confidence: 99%

Solving the missing node problem using structure and attribute information

Sina

Rosenfeld

Kraus

2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Abstract-An important area of social networks research is identifying missing information which is not explicitly represented in the network, or is not visible to all. Recently, the Missing Node Identification problem was introduced where missing members in the social network structure must be identified. However, previous works did not consider the possibility that information about specific users (nodes) within the network could be useful in solving this problem. In this paper, we present two algorithms: SAMI-A and SAMI-N. Both of these algorithms use the known nodes' specific information, such as demographic information and the nodes' historical behavior in the network. We found that both SAMI-A and SAMI-N perform significantly better than other missing node algorithms. However, as each of these algorithms and the parameters within these algorithms often perform better in specific problem instances, a mechanism is needed to select the best algorithm and the best variation within that algorithm. Towards this challenge, we also present OASCA, a novel online selection algorithm. We present results that detail the success of the algorithms presented within this paper.

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“…In this article, by partial observations we mean observing all the infection times of cascades, while the network is latent and we do not know who infected whom. Studies that aim to extract the diffusion network by incomplete observations, are usually known as structure learning or inference problems [Rodriguez et al 2010;Gripon and Rabbat 2013;Snowsill et al 2011;Adar and Adamic 2005;Gomez-Rodriguez et al 2011;Myers and Leskovec 2010;Eslami et al 2011;Zong et al 2012;Netrapalli and Sanghavi 2012;Chierichetti et al 2011;Sadikov et al 2011;Gomez-Rodriguez and Schölkopf 2012;Daneshmand et al 2014;Alpay et al 2011].…”

Section: Introductionmentioning

confidence: 99%

Inferring Dynamic Diffusion Networks in Online Media

Tahani

Hemmatyar

Rabiee

et al. 2016

ACM Trans. Knowl. Discov. Data

Self Cite

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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.

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DNE: A Method for Extracting Cascaded Diffusion Networks from Social Networks

Cited by 17 publications

References 22 publications

Predicting and Identifying Missing Node Information in Social Networks

Predicting and Identifying Missing Node Information in Social Networks

Solving the missing node problem using structure and attribute information

Inferring Dynamic Diffusion Networks in Online Media

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