Optimal detection of influential spreaders in online social networks

Tan, Chee Wei; Yu, Pei-Duo; Lai, Chun-Kiu; Zhang, Wenyi; Fu, Hung‐Lin

doi:10.1109/ciss.2016.7460492

Cited by 22 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These two estimators are closely related to the rumor centrality estimator. To be more precise, in the case of general tree, they are essentially the same as it is proved in [12] and [40] (Theorem 2) that…”

Section: The Basic Model: Quasi-regular Treementioning

confidence: 75%

See 1 more Smart Citation

An Algorithmic Framework for Estimating Rumor Sources With Different Start Times

Tay

Varshney

2017

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

We study the problem of identifying multiple rumor or infection sources in a network under the susceptibleinfected model, and where these sources may start infection spreading at different times. We introduce the notion of an abstract estimator, which given the infection graph, assigns a higher value to each vertex in the graph it considers more likely to be a rumor source. This includes several of the single-source estimators developed in the literature. We introduce the concepts of a quasi-regular tree and a heavy center, which allows us to develop an algorithmic framework that transforms an abstract estimator into a two-source joint estimator, in which the infection graph can be thought of as covered by overlapping infection regions. We show that our algorithm converges to a local optimum of the estimation function if the underlying network is a quasi-regular tree. We further extend our algorithm to more than two sources, and heuristically to general graphs. Simulation results on both synthetic and real-world networks suggest that our algorithmic framework outperforms several existing multiple-source estimators, which typically assume that all sources start infection spreading at the same time.

show abstract

Section: The Basic Model: Quasi-regular Treementioning

confidence: 75%

“…Detailed discussion of these estimators can be found in [12] and [40]. Our presentation is slightly different from their original forms in view of Definition 3(c), which always casts the detection problem as a maximizing problem.…”

Section: The Basic Model: Quasi-regular Treementioning

confidence: 99%

An Algorithmic Framework for Estimating Rumor Sources With Different Start Times

Tay

Varshney

2017

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Next, note that by choosing c 3 large enough, one can make t 2 smaller than t 1 . Now using (15) and (17), we can choose c 3 large enough such that for n large enough and any j = i, with probability at least (1 − ) we have:…”

Section: Figure 5: Casementioning

confidence: 99%

“…There are several notions of node centrality in graphs that have been proposed in the literature, such as distance centrality, betweenness centrality, degree centrality, and eigenvalue centrality (see for example [1,2]). These centrality measures find application in a wide variety of contexts, such as identifying influential/critical entities in social/communication networks [15,16], source/root detection in diffusion/growing networks [4,10,12,13], and facility location problems [14,22,23].…”

Section: Introductionmentioning

confidence: 99%

Persistence of the Jordan Center in Random Growing Trees

Pattathil

Karamchandani

Shah

2018

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

View full text Add to dashboard Cite

The Jordan center of a graph is defined as a vertex whose maximum distance to other nodes in the graph is minimal, and it finds applications in facility location and source detection problems. We study properties of the Jordan center in the case of random growing trees. In particular, we consider a regular tree graph on which an infection starts from a root node and then spreads along the edges of the graph according to various random spread models. For the Independent Cascade (IC) model and the discrete Susceptible Infected (SI) model, both of which are discrete time models, we show that as the infected subgraph grows with time, the Jordan center persists on a single vertex after a finite number of timesteps. Finally, we also study the continuous time version of the SI model and bound the maximum distance between the Jordan center and the root node at any time.

show abstract

“…One possible approach is to maximize a real-valued estimator function e(s, T ) over each candidate source node s ∈ V , and each tree T rooted at s that spans the infected nodes U . To make this optimization procedure computationally tractable for a general graph, for each candidate source node s, a random breadth-first search (BFS) tree T BFS (s) is usually chosen (e.g., [9], [13], [18]), and the source node is estimated by arg max s∈V e(s, T BFS (s)).…”

Section: Introductionmentioning

confidence: 99%

On the Properties of Gromov Matrices and Their Applications in Network Inference

Tang

Tay

2019

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

The spanning tree heuristic is a commonly adopted procedure in network inference and estimation. It allows one to generalize an inference method developed for trees, which is usually based on a statistically rigorous approach, to a heuristic procedure for general graphs by (usually randomly) choosing a spanning tree in the graph to apply the approach developed for trees. However, there are an intractable number of spanning trees in a dense graph. In this paper, we represent a weighted tree with a matrix, which we call a Gromov matrix. We propose a method that constructs a family of Gromov matrices using convex combinations, which can be used for inference and estimation instead of a randomly selected spanning tree. This procedure increases the size of the candidate set and hence enhances the performance of the classical spanning tree heuristic. On the other hand, our new scheme is based on simple algebraic constructions using matrices, and hence is still computationally tractable. We discuss some applications on network inference and estimation to demonstrate the usefulness of the proposed method. Index TermsGromov matrix, spanning tree, network inference and estimation

show abstract

Optimal detection of influential spreaders in online social networks

Cited by 22 publications

References 19 publications

An Algorithmic Framework for Estimating Rumor Sources With Different Start Times

An Algorithmic Framework for Estimating Rumor Sources With Different Start Times

Persistence of the Jordan Center in Random Growing Trees

On the Properties of Gromov Matrices and Their Applications in Network Inference

Contact Info

Product

Resources

About