Overlapping community-based fair influence maximization in social networks under open-source development model algorithm

Hurst exponent based approach for influence maximization in social networks

Influence maximization (IM) in online social networks (OSNs) has been extensively studied in the past few years, owing to its potential of impacting online marketing. IM aims at solving the problem of selecting a small set of influential nodes, who can lead to maximum influence spread across a social network. An integral part of IM is the modelling of the underlying diffusion process, which has a substantial impact on the spread achieved by any seed set. In this paper, Hurst-based diffusion model for IM has been proposed, under which node’s activation depends upon the nature of self-similarity exhibited in its past activity pattern. Assessment of the self-similarity trend exhibited by a node’s activity pattern, has been done using Hurst exponent (H). On the basis of the results achieved, the proposed model has been found to perform significantly better than two widely popular diffusion models, Independent Cascade and Linear Threshold, which are often used for IM in OSNs.

Influence diffusion and influence maximization in large-scale online social networks (OSNs) have been extensively studied because of their impacts on enabling effective online viral marketing. Existing studies focus on social networks with only friendship relations, whereas the foe or enemy relations that commonly exist in many OSNs, e.g., Epinions and Slashdot, are completely ignored. In this paper, we make the first attempt to investigate the influence diffusion and influence maximization in OSNs with both friend and foe relations, which are modeled using positive and negative edges on signed networks. In particular, we extend the classic voter model to signed networks and analyze the dynamics of influence diffusion of two opposite opinions. We first provide systematic characterization of both short-term and long-term dynamics of influence diffusion in this model, and illustrate that the steady state behaviors of the dynamics depend on three types of graph structures, which we refer to as balanced graphs, anti-balanced graphs, and strictly unbalanced graphs. We then apply our results to solve the influence maximization problem and develop efficient algorithms to select initial seeds of one opinion that maximize either its short-term influence coverage or long-term steady state influence coverage. Extensive simulation results on both synthetic and real-world networks, such as Epinions and Slashdot, confirm our theoretical analysis on influence diffusion dynamics, and demonstrate that our influence maximization algorithms perform consistently better than other heuristic algorithms.

Neural attentive influence maximization model in social networks via reverse influence sampling on historical behavior sequences

Influence Maximization (IM) in online social networks is a well-known complex optimization problem, having significant applications in viral marketing and E-commerce. This paper proposes a novel meta-heuristic driven solution methodology inspired from the shuffled searching behavior of Flamingos for the IM problem under “K” seeding budget constraint. The proposed approach intelligently amalgamates varied structural heuristics of networks such as communities, closeness and betweenness during iterations of optimization to heuristically prune the search space and accelerate convergence. The expected diffusion value function is also incorporated in our proposed approach using the Independent Cascade (IC) model to quantify reliable estimates of influence spreads. A detailed experimentation is conducted for constrained parameter “K” (seed set size) ranging seed set values in the range 10–50. An exhaustive comparative analysis of the results generated by our proposed approach with seven state-of-the-art IM approaches on five real-world social networks in the size ranges small (few hundred) to large (in thousands) is performed. The comparative analysis provides strong evidence for the significantly improved magnitude of spread estimates by the seeds generated by our proposed approach in contrast to many state-of-the-art IM solution methodologies proving its efficacy.

Overlapping community‐based particle swarm optimization algorithm for influence maximization in social networks

Influence maximization, a classic optimization problem in social networks, targets to find a set of influential users capable of producing maximum influence spread under a particular diffusion model. Community structure, an important topological property of social networks, also plays a significant role in various dynamical processes, including the spreading of information in networks. Realizing the impact of community structure in information spreading, some community-based influence maximization methods have been proposed recently, and these studies show that community-based influence maximization methods perform better than the traditional influence maximization. However, the impact of the method used to detect communities on influence spread in the influence maximization problem is unknown, the need to verify and find the most effective community detection method suitable for influence maximization is essential. This paper addresses this problem by computing the influence spread of k influential nodes selected by an influential node selection method from the k largest communities extracted by the various community detection algorithms on a network. Experiments have been conducted to evaluate the performance of community detection algorithms and their impact on influence maximization on both artificial and real-world networks by employing four heuristic influence maximization methods under IC diffusion model.

TIFIM: A Two-stage Iterative Framework for Influence Maximization in Social Networks

Influence maximization (IM) in social networks aims to figure out the best subset of seed nodes which have maximum cascading influence under a diffusion model. This paper proposes a hybrid Community-based Simulated Annealing (ComSA) approach for the IM problem. A community detection algorithm is employed to segregate the entire social network structure into some more deeply clustered communities. Thereafter, a degree-based metric has been used to select the candidate pool from each community by excluding less influential nodes at the preliminary data preprocessing phase. A community-based seed initialization and neighborhood search technique have been proposed. To speed up the convergence of stable solutions in Simulated Annealing approach, a greedy hill climbing strategy is also implemented instead of using probabilistic based solution acceptance processes. Experimental results on four real-world datasets show that our proposed algorithm has comparable solution with greedy and outperforms the other existing meta-heuristic approaches.

Trust based latency aware influence maximization in social networks

Our study concerns an important current problem, that of influence maximization in social network. This problem has received significant attention from the researchers recently, driven by many potential applications such as viral marketing and sales promotion. It is a fundamental issue to find a subset of key individuals in a social network such that targeting them initially (e.g. to adopt a new product) will maximize the spread of the influence (further adoptions of the new product). However, the problem of finding the most key nodes is unfortunately NP-hard. It has been shown that a greedy algorithm with provable approximation guarantees can give good approximation. However, it is computationally expensive, if not prohibitive, to run the greedy algorithm on a large social network. Based on the community structure property of social network, a cooperative game theoretic algorithm CGINA to find key nodes is proposed. The proposed algorithm encompasses two stages. Firstly, we detect the community structure of the social network with the topological structure and information diffusion model. In this paper, we adopt the algorithm 2 in [4]. Then, we will find key nodes in communities. In this paper, we think of the information diffusion in the whole network as a cooperative game with transferable utility. The communities of the network happen to be the players in this cooperative game. With the solution concept Shapley value in game theory, we allocate the number of key nodes to discover in the community. Moreover, different from the existing relevant literature, in my opinion, the key nodes two parts. One is composed of “bridge” nodes, which contact other communities densely, and are easy to disseminate information across communities, the other is composed of “influential” nodes, which are at the core of the community, in close touch with other nodes, and can diffuse information quickly within the community. To adjust the ratio of these two kinds of nodes in the same community, a heuristic factor l is introduced. Empirical studies on a large social network show that our algorithm is efficient and powerful.

The influence maximization problem is aimed at finding a small subset of nodes in a social./network to maximize the expected number of nodes influenced by these nodes. Influence maximization plays an important role in viral marketing and information diffusion. However, some existing algorithms for influence maximization in social networks perform badly in either efficiency or accuracy. In this paper, we put forward an efficient algorithm, called a two-stage selection for influence maximization in social networks (TSIM). Moreover, a discount-degree descending technology and lazy-forward technology are proposed, called DDLF, to select a certain number of influential nodes as candidate nodes. Firstly, we utilize the strategy to select a certain number of nodes as candidate nodes. Secondly, this paper proposes the maximum influence value function to estimate the marginal influence of each candidate node. Finally, we select seed nodes from candidate nodes according to their maximum influence value. The experimental results on six real-world social networks show that the proposed algorithm outperforms other contrast algorithms while considering accuracy and efficiency comprehensively.

Influence maximization in social networks is the problem of finding a set of seed nodes in the network that maximizes the spread of influence under certain information prorogation model, which has become an important topic in social network analysis. In this paper, we show that conventional influence maximization algorithms cause uneven spread of influence among different attribute groups in social networks, which could lead to severer bias in public opinion dissemination and viral marketing. We formulate the balanced influence maximization problem to address the trade-off between influence maximization and attribute balance, and propose a sampling based solution to solve the problem efficiently. To avoid full network exploration, we first propose an attribute-based (AB) sampling method to sample attributed social networks with respect to preserving network structural properties and attribute proportion among user groups. Then we propose an attributed-based reverse influence sampling (AB-RIS) algorithm to select seed nodes from the sampled graph. The proposed AB-RIS algorithm runs efficiently with guaranteed accuracy, and achieves the trade-off between influence maximization and attribute balance. Extensive experiments based on four real-world social network datasets show that AB-RIS significantly outperforms the state-of-the-art approaches in balanced influence maximization.

Semantics-aware influence maximization in social networks

Influence maximization (IM) aims to find a subset of k nodes that can maximize the final active node set under an information diffusion model. With the development and popularity of social networks, the IM problem plays an essential role in various applications, such as public opinion analysis, viral marketing, and rumor early warning. However, most of the existing IM solutions have not accessed the risk of negative information from nodes in the future. In reality, a company may face economic loss when negative information breaks out of its spokesman. Therefore, a crisis assessment oriented and topic-based IM problem (TIM-CA) is proposed, which is utilized to model the IM problem by considering the crisis assessment (CA) and topics of users. To solve this problem, we propose a maximum influence arborescence model-based algorithm for TIM-CA, namely, MIA-TIM-CA. The proposed algorithm consists of crisis degree calculation, topic relevance calculation, and influence spread evaluation. More importantly, as for crisis degree calculation, it considers self-, topology-, and topic-based crisis degrees for each node. At the influence spread evaluation stage, MIA-TIM-CA proposes two functions to evaluate the node's importance and node influence spread. Extensive experiments on two real-world social networks demonstrate that our MIA-TIM-CA outperforms all comparison algorithms on influence spread, crisis score, and running time.