Influence maximization: a local branching algorithm for solving the positive influence dominating set problem

An ILP based memetic algorithm for finding minimum positive influence dominating sets in social networks

An efficient local search algorithm for minimum positive influence dominating set problem

Construct, Merge, Solve & Adapt (CMSA) is a recently developed algorithm for solving combinatorial optimization problems. It combines heuristic elements, such as the probabilistic generation of solutions, with an exact solver that is iteratively applied to sub-instances of the tackled problem instance. In this paper, we present the application of CMSA to an NP-hard problem from the family of dominating set problems in undirected graphs. More specifically, the application in this paper concerns the minimum positive influence dominating set problem, which has applications in social networks. The obtained results show that CMSA outperforms the current state-of-the-art metaheuristics from the literature. Moreover, when instances of small and medium size are concerned CMSA finds many of the optimal solutions provided by CPLEX, while it clearly outperforms CPLEX in the context of the four largest, respectively more complicated, problem instances.

The minimum positive influence dominating set problem is one of the central problems in the study of online social networks. This paper presents a hybrid swarm intelligence-based algorithm to solve the minimum positive influence dominating set problem. The proposed swarm intelligence-based algorithm is based on genetic algorithm and particle swarm optimization. Firstly, a greedy randomized adaptive construction procedure is employed to generate initial swarm. Secondly, a crossover procedure is applied to obtain new solutions. Then, a mutation procedure is introduced to diversify the population. Finally, a repair procedure is used to ensure the feasibility of new solutions. Nine social networks from the literature are applied to test the proposed algorithm. The experimental results show that the proposed algorithm can achieve significant improvements over the existing greedy algorithms.

Recent research has shown that adding negative learning to ant colony optimization, in addition to the traditional positive learning mechanism, may improve the algorithms' performance significantly. In this paper we consider the application of this novel ant colony optimization variant to an NP-hard combinatorial optimization problem known as the minimum positive influence dominating set problem. This problem has applications especially in the context of social networks. Our results show, first, that the negative learning variant significantly improves over the standard ant colony optimization variant. Second, the obtained results show that our algorithm outperforms all competitors from the literature.

Construct, merge, solve and adapt (CMSA) is a recently developed, generic algorithm for combinatorial optimisation. Even though the usefulness of the algorithm has been demonstrated by applications to a range of combinatorial optimisation problems, in some applications, it was observed that the algorithm can be sensitive to parameter settings. In this work, we propose a self-adaptive variant of CMSA, called Adapt-CMSA, with the aim of reducing the parameter sensitivity of the original version of CMSA. The advantages of this new CMSA variant are demonstrated in the context of the application to the so-called minimum positive influence dominating set problem. It is shown that, in contrast to CMSA, Adapt-CMSA does not require a computation time intensive parameter tuning process for subsets of the considered set of problem instances. In fact, after tuning Adapt-CMSA only once for the whole set of benchmark instances, the algorithm already obtains state-of-the-art results. Nevertheless, note that the main objective of this paper is not the tackled problem but the improvement of CMSA.

Motivated by applications in social networks, a new type of dominating set named Positive Influence Dominating Set (PIDS) has been studied in the literature. In this paper, we investigate cooperative cost games arising from PIDS problem on social network graphs. We propose two new game models, Rigid PIDS Game and Relaxed PIDS Game, and focus on their cores. First, a relationship between the cores of both games is obtained. Next, we also prove that the core of the relaxed PIDS game is nonempty if and only if there is no integrality gap for the relaxation linear programming of the PIDS problem on graph G.

This paper presents a performance comparison of greedy heuristics for a recent variant of the dominating set problem known as the minimum positive influence dominating set (MPIDS) problem. This APX-hard combinatorial optimization problem has applications in social networks. Its aim is to identify a small subset of key influential individuals in order to facilitate the spread of positive influence in the whole network. In this paper, we focus on the development of a fast and effective greedy heuristic for the MPIDS problem, because greedy heuristics are an essential component of more sophisticated metaheuristics. Thus, the development of well-working greedy heuristics supports the development of efficient metaheuristics. Extensive experiments conducted on a wide range of social networks and complex networks confirm the overall superiority of our greedy algorithm over its competitors, especially when the problem size becomes large. Moreover, we compare our algorithm with the integer linear programming solver CPLEX. While the performance of CPLEX is very strong for small and medium-sized networks, it reaches its limits when being applied to the largest networks. However, even in the context of small and medium-sized networks, our greedy algorithm is only 2.53% worse than CPLEX.

As a variant of the dominating set problem, the positive influence dominating set problem tries to find a set such that at least half neighbors of every node in the graph are included by it. Because this problem has wide applications in social networks, it has received more and more attention. This paper presents a fast greedy algorithm to solve the problem. Sufficient experiments on typical real networks show that compared with the existing algorithms, the quality of the solutions obtained by the algorithm is close to or even better, while the running time is greatly reduced. Especially, when the network becomes larger, the running time can be reduced by several orders of magnitude.

We are proposing two greedy and a new linear programming based approximation algorithm for the total positive influence dominating set problem in weighted networks. Applications of this problem in weighted settings include finding: a minimum cost set of nodes to broadcast a message in social networks, such that each node has majority of neighbours broadcasting that message; a maximum trusted set in bitcoin network; an optimal set of hosts when running distributed apps etc.. Extensive experiments on different generated and real networks highlight advantages and potential issues for each algorithm.

Online social networks are increasingly connecting people around the world. Influence maximization is a key area of research in online social networks, which identifies influential users during information dissemination. Most of the existing influence maximization methods only consider the transmission of a single channel, but real-world networks mostly include multiple channels of information transmission with competitive relationships. The problem of influence maximization in an environment involves selecting the seed node set for certain competitive information, so that it can avoid the influence of other information, and ultimately affect the largest set of nodes in the network. In this paper, the influence calculation of nodes is achieved according to the local community discovery algorithm, which is based on community dispersion and the characteristics of dynamic community structure. Furthermore, considering two various competitive information dissemination cases as an example, a solution is designed for self-interested information based on the assumption that the seed node set of competitive information is known, and a novel influence maximization algorithm of node avoidance based on user interest is proposed. Experiments conducted based on real-world Twitter dataset demonstrates the efficiency of our proposed algorithm in terms of accuracy and time against notable influence maximization algorithms.

Influence maximization is a classic network optimization problem, which has been widely used in the field of viral marketing. The influence maximization problem aims to find a fixed number of active nodes. After a specific propagation model, the number of active nodes reaches the maximum. However, the existing influence maximization algorithms are overly pursuing certain indicators of efficiency or accuracy, which cannot be well accepted by some researchers. This paper proposes an effective algorithm to balance the accuracy and efficiency of the influence maximization problem called local two-hop search algorithm (LTHS). The core of the proposed algorithm is a node not only be affected by one-hop neighbor nodes, but also by two-hop neighbor nodes. Firstly, this paper selects initial seed nodes according to the characteristics of the node degree. Generally, the high degree of nodes regards as influential nodes. Secondly, this paper proposes a node two-hop influence evaluate function called two-hop diffusion value (THDV), which can evaluate node influence more accurately. Furthermore, in order to seek higher efficiency, this paper proposes a method to reduce the network scale. This paper conducted full experiments on five real-world social network datasets, and compared with other four well-known algorithms. The experimental results show that the LTHS algorithm is better than the comparison algorithms in terms of efficiency and accuracy.

The problem of Influence Maximization (IM) in social network refers to determining a set of nodes that can maximize the spread of influence. IM problem has been applied in many domains such as marketing, advertising and public opinion monitoring. In recent years, different type of algorithms reported in the literature. A type of algorithms for this problem is based on dominating set. In these algorithms, the IM problem is considered as a version of dominating set problem. A limitation of most of these algorithms, the graph of the social network is assumed to be a un-weighted graph which is not a realistic assumption. The other drawback of these algorithms is that they generally omit the crucial characteristics of the social networks such as different level of influence and dynamic interaction among individuals. In order to solve these problems and based on the realistic nature of the social network, it seems that the Minimum Weighted Positive Influence Dominating Set (MWPIDS) problem can support the mentioned characteristics because it consider the weight of the graph. In this paper, a learning automaton based algorithm is proposed to reveal MWPIDS in the social network graphs. In the proposed algorithm, each vertex of the social network graph is equipped with a learning automaton that determines the beeing candidate or non-candidate of the corresponding vertex to be in WPIDS or not. Owing to adaptive decision making characteristics of learning automata, the proposed algorithm significantly reduces the number of candidate solution. The proposed algorithm, based on learning automata, iteratively decreases the weight of the obtained positive influence dominating. In order to evaluate the proposed algorithm, several experiments have been conducted on real social network datasets which compared to the state-of-the art methods. Experimental results show the superiority of the proposed algorithm over the previous algorithms.

Positive influence maximization in signed social networks based on simulated annealing

In recent years, with the rapid development of online social networks, an enormous amount of information has been generated and diffused by human interactions through online social networks. The availability of information diffused by users of online social networks has facilitated the investigation of information diffusion and influence maximization. In this paper, we focus on the influence maximization problem in social networks, which refers to the identification of a small subset of target nodes for maximizing the spread of influence under a given diffusion model. We first propose a learning automaton-based algorithm for solving the minimum positive influence dominating set (MPIDS) problem, and then use the MPIDS for influence maximization in online social networks. We also prove that by proper choice of the parameters of the algorithm, the probability of finding the MPIDS can be made as close to unity as possible. Experimental simulations on real and synthetic networks confirm the superiority of the algorithm for finding the MPIDS Experimental results also show that finding initial target seeds for influence maximization using the MPIDS outperforms well-known existing algorithms.