Deprecation based greedy strategy for target set selection in large scale social networks

Kundu, Suman; Pal, Sankar K.

doi:10.1016/j.ins.2015.04.024

Cited by 30 publications

(13 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chen et.al come up with two methods [16], named as NewGreedy and MixedGreedy, to improve the greedy algorithm. Kundu and Pal proposed a deprecation based greedy strategy [17], in this strategy, they estimate the performance of each node and mark the nodes to be deprecated. They also proved that if the influence function is monotonic and sub-modular, this strategy can provide a guaranteed seed set.…”

Section: A Related Workmentioning

confidence: 99%

Budgeted Influence Maximization via Boost Simulated Annealing in Social Networks

Wu¹,

Gao²,

Zhu³

et al. 2022

Preprint

View full text Add to dashboard Cite

Due to much closer to real application scenarios, the budgeted influence maximization (BIM) problem has attracted great attention among researchers. As a variant of the influence maximization (IM) problem, the BIM problem aims at mining several nodes with different costs as seeds with limited budget to maximize the influence as possible. By first activating these seed nodes and spreading influence under the given propagation model, the maximized spread of influence can be reached in the network.Several approaches have been proposed for BIM. Most of them are modified versions of the greedy algorithm, which work well on the IM but seems inefficient for the BIM because huge time consuming is inevitable. Recently, some intelligence algorithms are proposed in order to reduce the running time, but analysis shows that they cannot fully utilize the relationships between nodes in networks, which will result in influence loss.Inspired by this, we propose an efficient method based on boosted simulated annealing (SA) algorithm in this paper. Three heuristic strategies are proposed to improve the performance and speed up the proposed algorithm. Experimental results on both real world and synthetic networks demonstrate that the proposed boosted SA performs much better than existed algorithms on performance with almost equal or less running time.

show abstract

Section: A Related Workmentioning

confidence: 99%

Budgeted Influence Maximization via Boost Simulated Annealing in Social Networks

Wu¹,

Gao²,

Zhu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…[23] developed an improved version of CELF, called CELF++, and showed that it is 35-55% faster than CELF. [24] proposed a deprecation based greedy algorithm for the IMP, called DGS. This algorithm first orders the nodes of a social network by applying three heuristic influence functions, and then selects the most influential nodes from a list of pre-ordered vertices.…”

Section: Greedy Based Approachmentioning

confidence: 99%

“…Then, remove u from S, and select a vertex v from T randomly (for the diversity) and add it to S (lines [17][18]. If the exchanging can produce more influence, then it is viewed as a valid process, and update S* by S and T by T ∪ {u} (for the diversity of solutions) (lines 19-21); otherwise, S is back to the previous state (lines [22][23][24].…”

Section: The Domim Algorithmmentioning

confidence: 99%

A Local Search Algorithm for the Influence Maximization Problem

Zhu

Yang

2021

Front. Phys.

View full text Add to dashboard Cite

How to select a set of top k nodes (called seeds) in a social network, through which the spread of influence under some certain diffusion models can achieve the maximum, is a major issue considered in the social network analysis. This problem is known as the Influence Maximization Problem (IMP). Due to its NP-hard nature, designing a “good” algorithm for the IMP is a very challengeable work. In this paper, we propose an efficient local search algorithm called DomIM to solve the IMP, which involves two main ideas. The first one is an approach to constructing an initial solution based on a dominating set, while the second is a degree based greedy strategy in the local search phase. DomIM is evaluated on three real world networks, under three widely-used diffusion models, including independent cascade (IC) model, weighted cascade (WC) model, and linear threshold (LT) model. Experimental results show that DomIM is competitive and efficient, and under all of these diffusion models it can obtain the best performance (in terms of solution quality) on the networks we consider.

show abstract

“…Amazon0302 [34] D 262111 1234877 420 262111 0.4198 0.6697 BlogCatalog [42] U The competing algorithms. Several heuristics devoted to compute small size target sets have been proposed in the literature; they are typically classified in: additive algorithms [8,9,29] and subtractive algorithms [17,39,31] (depending on whether they focus on the addition of nodes to the target set or removal of nodes from the network). Additive algorithms typically follow a greedy strategy which adds iteratively a node to a set S until S becomes a target set.…”

Section: Namementioning

confidence: 99%

On finding small sets that influence large networks

Cordasco

Gargano

Rescigno

2016

Soc. Netw. Anal. Min.

View full text Add to dashboard Cite

We consider the problem of selecting a minimum size subset of nodes in a network, that allows to activate all the nodes of the network. We present a fast and simple algorithm that, in real-life networks, produces solutions that outperform the ones obtained by using the best algorithms in the literature. We also investigate the theoretical performances of our algorithm and give proofs of optimality for some classes of graphs. From an experimental perspective, experiments also show that the performance of the algorithms correlates with the modularity of the analyzed network. Moreover, the more the influence among communities is hard to propagate, the less the performances of the algorithms differ. On the other hand, when the network allows some propagation of influence between different communities, the gap between the solutions returned by the proposed algorithm and by the previous algorithms in the literature increases.

show abstract

Deprecation based greedy strategy for target set selection in large scale social networks

Cited by 30 publications

References 21 publications

Budgeted Influence Maximization via Boost Simulated Annealing in Social Networks

Budgeted Influence Maximization via Boost Simulated Annealing in Social Networks

A Local Search Algorithm for the Influence Maximization Problem

On finding small sets that influence large networks

Contact Info

Product

Resources

About