Influence Diffusion In Social Networks Research Articles

DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)

Influence Maximization is the task of selecting optimal nodes maximising the influence spread in social networks. This study proposes a Discretized Quantum-based Salp Swarm Algorithm (DQSSA) for optimizing influence diffusion in social networks. By discretizing meta-heuristic algorithms and infusing them with quantum-inspired enhancements, we address issues like premature convergence and low efficacy. The proposed method, guided by quantum principles, offers a promising solution for Influence Maximisation. Experiments on four real-world datasets reveal DQSSA's superior performance as compared to established cutting-edge algorithms.

Structured multiagent decision-making in information diffusion: The model and dynamics

In recent years, studies in many fields have widely explored diffusion in social networks, which refers to the collective behavior of a set of autonomous social actors who interact with something (such as opinions, viruses, or innovations) in social networks. Many researchers have utilized a multiagent method to explore the effects of social actor characteristics and behavioral complexity on information diffusion. This method performs well in modeling the complex characteristics and decision-making of social actors. Although this subject has been intensively studied, analyses have typically been limited to concerns about the effects of entire social network structures, such as the small-world and scale-free aspects. Few empirical information diffusion studies have systematically investigated causal effects and decision-making mechanisms under the constraints of the interaction structures of agents. In practice, we observed that the decision-making of each agent in information diffusion was significantly influenced and constrained by its contextual network structures. To solve this problem, this study utilizes the cause–effect graph method to model the interaction structures of agents in social networks and presents agent decision-making models under the constraints of local network structures in information diffusion. The proposed models and methods can be used to effectively analyze the mechanism of the influence of structured interaction relations of agents on the process of decision-making and diffusion results. Moreover, the presented decision-making model can comprehensively consider the factors of social positions of heterogeneous agents, interaction strengths, and diffusion strategies, and thus, can better match the features of real-world influence diffusion in social networks.

Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process

Influence maximization is a problem of finding a small set of highly influential users in a social network such that the spread of influence under certain propagation models is maximized. In this paper, we consider time-critical influence maximization, in which one wants to maximize influence spread within a given deadline. Since timing is considered in the optimization, we also extend the Independent Cascade (IC) model to incorporate the time delay aspect of influence diffusion in social networks. We show that time-critical influence maximization under the time-delayed IC model maintains desired properties such as submodularity, which allows a greedy algorithm to achieve an approximation ratio of 1-1/e, to circumvent the NP-hardness of the problem. To overcome the inefficiency of the approximation algorithm, we design two heuristic algorithms: the first one is based on a dynamic programming procedure that computes exact influence in tree structures, while the second one converts the problem to one in the original IC model and then applies existing fast heuristics to it. Our simulation results demonstrate that our heuristics achieve the same level of influence spread as the greedy algorithm while running a few orders of magnitude faster, and they also outperform existing algorithms that disregard the deadline constraint and delays in diffusion.

Fast and frugal targeting with incentives

A widely studied model of influence diffusion in social networks represents the network as a graph G=(V,E), with an integer influence threshold t(v) for each node, and the diffusion process as follows: Initially the members of a chosen set S⊆V are influenced and, during each subsequent round, the set of influenced nodes is augmented by including every new node v that has at least t(v) previously influenced neighbours. The general problem is to find a small initial set that influences the whole network. In this paper we extend this model by using incentives to reduce the thresholds of some nodes. The goal is to minimize the total amount of the incentive required to ensure that the information diffusion process terminates within a given number of rounds λ. The problem is hard to approximate in general networks. We present optimal polynomial-time algorithms for paths, cycles, trees, and complete networks for any λ. For the special case λ=1, we present a polynomial-time algorithm with a logarithmic approximation guarantee for any network.

On some tractable and hard instances for partial incentives and target set selection

A widely studied model for influence diffusion in socialnetworks are target sets. For a graph G and an integer-valued threshold function τ on its vertex set, a target set or dynamic monopoly is a set of vertices of G such that iteratively adding to it vertices u of G that have at least τ(u) neighbors in it eventually yields the entire vertex set of G. This notion is limited to the binary choice of including a vertex in the target set or not, and Cordasco, Gargano, Rescigno, and Vaccaro proposed partial incentives as a variant allowing for intermediate choices.We show that finding optimal partial incentives is hard for chordal graphs and planar graphs but tractable for graphs of bounded treewidth and for interval graphs with bounded thresholds. We also contribute some new results about target set selection on planar graphs by showing the hardness of this problem, and by describing an efficient O(n)-approximation algorithm as well as a PTAS for the dual problem of finding a maximum degenerate set.

A gradient-based methodology for optimizing time for influence diffusion in social networks

In social network analysis, one of the significant problems is finding the most influential entities within the network, which has proved to be NP-hard. The problem of influence maximization in a social network is an optimization problem that ensures that the spread of influence in the network is maximized. Although many algorithms have been proposed for influence maximization, most of them provide influence spread, at the cost of execution time. Therefore, a novel methodology based on gradient approach is proposed in this paper to deal with the problem. This approach provides a balance between influence spread and execution time. In this research, the performance of the proposed algorithm has been compared with existing algorithms and observations of a better influence spread per second are presented. This task has significance in viral marketing, since the most influential entities can be targeted for endorsing new products in the market at a faster rate.

Modeling and maximizing influence diffusion in social networks for viral marketing

Modeling influence diffusion in social networks is an important challenge. We investigate influence-diffusion modeling and maximization in the setting of viral marketing, in which a node’s influence is measured by the number of nodes it can activate to adopt a new technology or purchase a new product. One of the fundamental problems in viral marketing is to find a small set of initial adopters who can trigger the most further adoptions through word-of-mouth-based influence propagation in the network. We propose a novel multiple-path asynchronous threshold (MAT) model, in which we quantify influence and track its diffusion and aggregation. Our MAT model captures not only direct influence from neighboring influencers but also indirect influence passed along by messengers. Moreover, our MAT framework models influence attenuation along diffusion paths, temporal influence decay, and individual diffusion dynamics. Our work is an important step toward a more realistic diffusion model. Further, we develop an effective and efficient heuristic to tackle the influence-maximization problem. Our experiments on four real-life networks demonstrate its excellent performance in terms of both influence spread and time efficiency. Our work provides preliminary but significant insights and implications for diffusion research and marketing practice.

A community-based algorithm for influence blocking maximization in social networks

With the increasing popularity of social networking sites and the convenience of information diffusion in social network, social network has been a huge platform for information diffusion and knowledge sharing. However, the incapability of the supervision over the content of networks usually leads to the threats of negative influence, which may lead to undesirable effects. Influence blocking maximization (IBM) problem which aims to find a subset of nodes that need to adopt the positive influence (L) to minimize the number of nodes that adopt the negative influence (C) at the end of both propagation processes is addressed in this work. Under a well-known Campaign-Oblivious Independent Cascade Model, the objective function of IBM is submodular, and thus an approximation algorithm Greedy is obtained. Subsequently, based on the locality of influence diffusion in social networks, an efficient algorithm CB_IBM is proposed, which is based on the community structure of the network. Extensive simulations of CB_IBM, Greedy, and other baseline algorithms have been conducted on two real-world datasets, and experiments show that, in terms of the blocking effect, CB_IBM consistently matches the performance of Greedy, however, it is much faster than Greedy.

Towards intelligent control of influence diffusion in social networks

Control of the flow of information in large-scale non-deterministic social networks is a complex problem requiring both a search for the optimal connection of a control system to the network, and a means of determining the required control signals. This paper formalizes the Network Control Problem (NCP) as a means of relating the field of diverse social network control subproblems. Additionally, this paper defines a novel NCP subproblem, the \(\theta \)-Consensus Avoidance Problem (\(\theta \)-CAP), as a next step towards solving the general NCP. Benchmark results for the \(\theta \)-CAP using Artificial Neural Networks, Evolutionary Neural Networks, and heuristic methods are presented, and interesting areas of the problem space are identified.

Influence diffusion in social networks under time window constraints

We study a combinatorial model of the spread of influence in networks that generalizes existing schemata recently proposed in the literature. In our model, agents change behaviours/opinions on the basis of information collected from their neighbours in a time interval of bounded size whereas agents are assumed to have unbounded memory in previously studied scenarios. In our mathematical framework, one is given a network G=(V,E), an integer value t(v) for each node v∈V, and a time window size λ. The goal is to determine a small set of nodes (target set) that influences the whole graph. The spread of influence proceeds in rounds as follows: initially all nodes in the target set are influenced; subsequently, in each round, any uninfluenced node v becomes influenced if the number of its neighbours that have been influenced in the previous λ rounds is greater than or equal to t(v). We prove that the problem of finding a minimum cardinality target set that influences the whole network G is hard to approximate within a polylogarithmic factor. On the positive side, we design exact polynomial time algorithms for paths, rings, and trees.

Maximizing rumor containment in social networks with constrained time

The spread of rumor or misinformation in social networks may cause bad effects among the public. Thus, it is necessary to find effective strategies to control the spread of rumor. Specifically, in our paper, we consider such a setting: initially, a subset of nodes is chosen as the set of protectors, and the influence of protector diffuses competitively with the diffusion of rumor. However, in real world, we generally have limited budget (limited number of protectors) and time to fight with rumor. Therefore, we study the problem of maximizing rumor containment within a fixed number of initial protectors and a given time deadline. Generalizing two seminal models in the field—the Independent Cascade (IC) model and the Linear Threshold (LT) model—we propose two new models of competitive influence diffusion in social networks with the following three factors: a time deadline for information diffusion, random time delay of information exchange and personal interests regarding the acceptance of information. Under these two models, we show that the optimization problems are NP-hard. Furthermore, we prove that the objective functions are submodular. As a result, the greedy algorithm is used as constant-factor approximation algorithms with performance guarantee \(1-\frac{1}{e}\) for the two optimization problems.