Modeling Information Diffusion in Networks with Unobserved Links

Duong, Quang; Wellman, Michael P.; Singh, Satinder

doi:10.1109/passat/socialcom.2011.50

Cited by 8 publications

(11 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To overcome this shortage of available information for the network reconstruction for SI and SIR models, a commonly employed setting is the observation of multiple, independent outbreaks or cascades [2]. The state-of-the-art network reconstruction methods for SI and SIR models confine to a maximum-likelihood formulation in discrete time [3], [4], [5], [6], [7], [8]. Besides network reconstruction methods based on observing viral dynamics, current research also focusses on other dynamical processes on networks.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Exact Network Reconstruction from Complete SIS Nodal State Infection Information Seems Infeasible

Prasse

Mieghem

2019

IEEE Trans. Netw. Sci. Eng.

View full text Add to dashboard Cite

The SIS dynamics of the spread of a virus crucially depend on both the network topology and the spreading parameters. Since neither the topology nor the spreading parameters are known for the majority of applications, they have to be inferred from observations of the viral spread. We propose an inference method for both topology and spreading parameters based on a maximum-a-posteriori estimation approach for the sampled-time Markov chain of an SIS process. The resulting estimation problem, given by a mixed-integer optimisation problem, results in exponential computational time if a brute-force approach is employed. By introducing an efficient and accurate, polynomial-time heuristic, the topology of the network can almost always be exactly reconstructed. Notwithstanding, reconstructing the network with a reasonably high accuracy requires a subexponentially increasing number of observations and an exponentially increasing computation time with respect to the number of nodes N. Such long observation periods are hardly realistic, which justifies the claim in the title.

show abstract

Section: Related Workmentioning

confidence: 99%

“…The upper bound on T in (13) is smaller than both bounds in (2) and (5) and, hence, condition (13) is a sufficient condition for all transition probabilities of the sampled-time Markov chain to lie in the interval [0, 1].…”

Section: Sampled-time -Sis Processmentioning

confidence: 99%

Exact Network Reconstruction from Complete SIS Nodal State Infection Information Seems Infeasible

Prasse

Mieghem

2019

IEEE Trans. Netw. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…These researchers have improved their model through concave optimization [15]. Duong et al [16] have resolved this problem using two approaches: The first learns graphical model potentials for a given network structure, compensating for missing edges through induced correlations among node states. The second learns the missing connections directly.…”

Section: Generic Approachesmentioning

confidence: 99%

A Genetic Programming based Algorithm for Predicting Exchanges in Electronic Trade using Social Networks’ Data

Poor¹,

Shiri²

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Purpose of this paper is to use Facebook dataset for predicting Exchanges in Electronic business. For this purpose, first a dataset is collected from Facebook users and this dataset is divided into two training and test datasets. First, an advertisement post is sent for training data users and feedback from each user is recorded. Then, a learning machine is designed and trained based on these feedbacks and users' profiles. In order to design this learning machine, genetic programming is used. Next, test dataset is used to test the learning machine. The efficiency of the proposed method is evaluated in terms of Precision, Accuracy, Recall and F-Measure. Experiment results showed that the proposed method outperforms basic algorithm (based on J48) and random selection method in selecting objective users for sending advertisements. The proposed method has obtained Accuracy=74% and 73% earning ration in classifying users.

show abstract

“…The problem of missing data in networks has been addressed from different perspectives ranging from network sampling [22,5,10], where the aim is to obtain a representative subset of the network, to network reconstruction [14,8], where nodes and edges are inferred to recreate the original network. Other works, such as [5,15], have examined the effect of missing nodes and edges on topological metrics of the network (e.g., average node degree, diameter, clustering coefficient).…”

Section: Related Workmentioning

confidence: 99%

“…Online social networks (OSNs) are a natural choice for this purpose as they are readily available and in many cases constitute the desired diffusion medium (e.g., Facebook and Twitter). Consequently such datasets have been widely used in previous research [27,8,23,20].…”

Section: Introductionmentioning

confidence: 99%

Phantom cascades: The effect of hidden nodes on information diffusion

Belák¹,

Mashhadi²,

Sala³

et al. 2016

Computer Communications

View full text Add to dashboard Cite

Research on information diffusion generally assumes complete knowledge of the underlying network. However, in the presence of factors such as increasing privacy awareness, restrictions on application programming interfaces (APIs) and sampling strategies, this assumption rarely holds in the real world which in turn leads to an underestimation of the size of information cascades. In this work we study the effect of hidden network structure on information diffusion processes. We characterise information cascades through activation paths traversing visible and hidden parts of the network. We quantify diffusion estimation error while varying the amount of hidden structure in five empirical and synthetic network datasets and demonstrate the effect of topological properties on this error. Finally, we suggest practical recommendations for practitioners and propose a model to predict the cascade size with minimal information regarding the underlying network.

show abstract

Modeling Information Diffusion in Networks with Unobserved Links

Cited by 8 publications

References 23 publications

Exact Network Reconstruction from Complete SIS Nodal State Infection Information Seems Infeasible

Exact Network Reconstruction from Complete SIS Nodal State Infection Information Seems Infeasible

A Genetic Programming based Algorithm for Predicting Exchanges in Electronic Trade using Social Networks’ Data

Phantom cascades: The effect of hidden nodes on information diffusion

Contact Info

Product

Resources

About