On Influential Nodes Tracking in Dynamic Social Networks

Chen, Xiao; Song, Guojie; He, Xinran; Xie, Kun

doi:10.1137/1.9781611974010.69

Cited by 30 publications

(32 citation statements)

References 14 publications

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“…Chen et al [6] considered the problem of tracking a seed set that maximizes the influence spread when a graph dynamically changes, and proposed a method that iteratively updates the current seed set. It should be noted that their method does not incorporate vertex additions and deletions.…”

Section: Influence Maximization In Dynamic Networkmentioning

confidence: 99%

Dynamic influence analysis in evolving networks

et al. 2016

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We propose the first real-time fully-dynamic index data structure designed for influence analysis on evolving networks. With this aim, we carefully redesign the data structure of the state-of-the-art sketching method introduced by Borgs et al., and construct corresponding update algorithms. Using this index, we present algorithms for two kinds of queries, influence estimation and influence maximization, which are strongly motivated by practical applications, such as viral marketing. We provide a thorough theoretical analysis, which guarantees the non-degeneracy of the solution accuracy after an arbitrary number of updates. Furthermore, we introduce a reachability-tree-based technique and a skipping method, which greatly reduce the time consumption required for edge/vertex deletions and vertex additions, respectively, and counter-based random number generators, which improve the space efficiency. Experimental evaluations using real dynamic networks with tens of millions of edges demonstrate the efficiency, scalability, and accuracy of our proposed indexing scheme. Specifically, it can reflect a graph modification within a time of several orders of magnitude smaller than that required to reconstruct an index from scratch, estimate the influence spread of a vertex set accurately within a millisecond, and select highly influential vertices at least ten times faster than state-of-the-art static algorithms.

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Section: Influence Maximization In Dynamic Networkmentioning

confidence: 99%

Dynamic influence analysis in evolving networks

et al. 2016

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“…To extract the influential users at time t, we set the parameters of IMM to be ε = 0.5, l = 1 [35] and run the algorithm on the generated influence graph Gt. • UBI [10]: We use the state-of-the-art method for IM on dynamic graphs as another baseline. The generation of influence graphs is the same as IMM.…”

Section: Methodsmentioning

confidence: 99%

“…However, most of these methods cannot provide a theoretical guarantee of their seed quality and may return arbitrarily bad solutions [1,39]. Chen et al [10] proposed an Upper Bound Interchange (UBI) method with a 1/2-approximation ratio. Nevertheless, UBI is sensitive to the number of users selected.…”

Section: Influence Maximization (Im)mentioning

confidence: 99%

“…Recently, there are some research efforts on IM under dynamic social influences. However, existing solutions for dynamic IM either do not offer theoretical guarantees for the qualities of the seeds selected (e.g., [1,39]) or provide guarantees at the expense of high processing overhead for updates (e.g., [10,29]). In fact, the state-of-the-art dynamic IM solution [29] is only able to process several hundred updates per second, which is far lower than the update rates of real-world social networks.…”

Section: Introductionmentioning

confidence: 99%

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Real-time influence maximization on dynamic social streams

Wang

Fan

et al. 2017

Proc. VLDB Endow.

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Influence maximization (IM), which selects a set of k users (called seeds) to maximize the influence spread over a social network, is a fundamental problem in a wide range of applications such as viral marketing and network monitoring. Existing IM solutions fail to consider the highly dynamic nature of social influence, which results in either poor seed qualities or long processing time when the network evolves. To address this problem, we define a novel IM query named Stream Influence Maximization (SIM) on social streams. Technically, SIM adopts the sliding window model and maintains a set of k seeds with the largest influence value over the most recent social actions. Next, we propose the Influential Checkpoints (IC) framework to facilitate continuous SIM query processing. The IC framework creates a checkpoint for each window slide and ensures an ε-approximate solution. To improve its efficiency, we further devise a Sparse Influential Checkpoints (SIC) framework which selectively keeps O( -approximate solution. Experimental results on both real-world and synthetic datasets confirm the effectiveness and efficiency of our proposed frameworks against the state-of-the-art IM approaches.

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“…Although some aspects of influence propagation in large networks have been extensively investigated, such as IM on static networks [2], [4], [5], [12]- [14], [17], [21], [24], there are only very limited studies on influence computation in dynamic networks [1], [6], most being heuristic. To the best of our knowledge, Ohsaka et al [20] and we [27] are the first to address influence computation in dynamic networks with provable quality guarantees.…”

Section: Introductionmentioning

confidence: 99%

Tracking Influential Individuals in Dynamic Networks

Yang

Wang

Pei

et al. 2017

IEEE Trans. Knowl. Data Eng.

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Influence propagation in networks has enjoyed fruitful applications and has been extensively studied in literature. However, only very limited preliminary studies tackled the challenges in handling highly dynamic changes in real networks. In this paper, we tackle the problem of tracking top-k influential vertices in dynamic networks, where the dynamic changes are modeled as a stream of edge weight updates. Under the popularly adopted linear threshold (LT) model and the independent cascade (IC) model, we address two essential versions of the problem: tracking the top-k influential individuals and finding the best k-seed set to maximize the influence spread (Influence Maximization). We adopt the polling-based method and maintain a sample of random RR sets so that we can approximate the influence of vertices with provable quality guarantees. It is known that updating RR sets over dynamic changes of a network can be easily done by a reservoir sampling method, so the key challenge is to efficiently decide how many RR sets are needed to achieve good quality guarantees. We use two simple signals, which both can be accessed in O(1) time, to decide a proper number of RR sets. We prove the effectiveness of our methods. For both tasks the error incurred in our method is only a multiplicative factor to the ground truth. For influence maximization, we also propose an efficient query algorithm for finding the k seeds, which is one order of magnitude faster than the state-of-the-art query algorithm in practice. In addition to the thorough theoretical results, our experimental results on large real networks clearly demonstrate the effectiveness and efficiency of our algorithms.

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On Influential Nodes Tracking in Dynamic Social Networks

Cited by 30 publications

References 14 publications

Dynamic influence analysis in evolving networks

Dynamic influence analysis in evolving networks

Real-time influence maximization on dynamic social streams

Tracking Influential Individuals in Dynamic Networks

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