Influence maximization (IM) is a key problem in social network analysis, with numerous applications in areas such as viral marketing, advertising, public health, and more. Current surveys in IM primarily focus on traditional algorithms while overlooking leading advanced IM algorithms such as signed IM, fairness-enabled IM, and diversity-based IM. This survey paper seeks to address the existing gap by providing a thorough review of both classical and advanced IM algorithms. It discusses classical techniques like simulation-based, path-based, and so on, and mainly focuses on recent advanced IM algorithms. In this detailed discussion of advanced algorithms, the work analyzes various aspects, including complexity, network types, properties (monotonicity, sub-modularity), diffusion models, and comparative algorithms, along with their advantages and disadvantages. This work includes an analysis of classical and advanced algorithms, as well as experimental evaluations of 20 state-of-the-art algorithms across 10 datasets. The chosen state-of-the-art algorithms utilize various approaches like simulation-based, evolutionary-based, fairness-based, and path-based methods. This work also addresses the challenges associated with IM and highlights emerging trends and future directions. Overall, this survey serves as a comprehensive guide to the latest developments in IM while proposing new avenues for future research in this field.

ABSTRACT The influence maximization (IM) problem focuses on identifying key nodes in social networks to maximize influence spread. IM is crucial in various applications such as viral marketing, information diffusion, advertisements, political campaigns, etc. Quantum‐inspired approaches enable the faster exploration of solutions. This work presents a quantum‐inspired genetic algorithm (QiGA) to address the IM problem. It maps the QiGA principles to solve the IM problem and proposes a novel strategy for encoding the influence diffusion rules using quantum circuits. This work evaluates the performance of the proposed algorithm against benchmark algorithms on synthetic and real‐world data sets. Experimental results demonstrate that the proposed algorithm outperforms existing algorithms on smaller networks and remains competitive in large‐scale networks. The execution time is acceptable, considering the quantum code is simulated on classical hardware. Thus, this research introduces quantum‐computing‐based QiGA algorithm to solve IM problem. Future research directions include implementing the algorithm on real quantum hardware and addressing IM using quantum computing‐based heuristics, such as node ranking, for further improvements.

Influence maximization (IM) is a widely studied topic in social network analysis that gives a reliable basis to select top nodes (seed set) to maximize the influence. IM has several real-world applications, such as advertising, political campaigns, profit maximization, etc. Existing literature suggests several algorithms for IM, including nature-inspired algorithms. In addition, most of the algorithms in IM consider static social networks. Existing studies show that antlion optimization (ALO) is known for its exploration abilities, and existing work in IM does not utilize it. Further, overlap influence reduces the overall influence in the network. To address the mentioned issues, for dynamic social networks, the proposed work suggests a novel algorithm (DALO-IM) for IM using ALO. The suggested strategy utilizes the previous computation during the dynamic traversal of the network. Further, this work suggests a prune-based strategy to overcome the problem of overlap influence. The experiments were conducted on eight datasets. The result analysis shows that the influence using the proposed algorithm is higher than the top-performing benchmark algorithm. Furthermore, this work conducted the ablation study to show the effectiveness of the suggested pruning strategy.

Influence maximization is a fundamental problem in network analysis, which attempts to identify a subset of nodes that maximizes the spread of influence/information. This problem has applications in various fields such as social networks, viral marketing, advertisements, and political campaigns, where understanding and exploiting network dynamics lead to effective strategies to promote behaviors, products, or ideas. The goal is to strategically select seed nodes to maximize the overall impact or adoption of an idea, behavior, or product in the network. Most of the existing IM algorithms give equal cost to selecting nodes and maximizing the active nodes, which overlook the number of influenced communities. To make it applicable to real-world applications, this article presents a diversified budgeted influence maximization (DBIM) algorithm for dynamic social networks. The DBIM algorithm considers the different costs of the nodes and maximizes the number of communities. This work proposes an objective function for diversification and presents an algorithm that finds the seed set utilizing the suggested strategy. Further, we show the monotone, submodular, and NP-hardness properties of the objective function. The proposed work experimentally shows the results on eight datasets and concludes that the proposed algorithm outperforms the activated nodes and the number of communities on all the datasets.

Criminal networks are the social networks that show the individual and their links involved in illegal activities. The influence maximization (IM) problem aims to find the top influential nodes in social networks. Existing studies of criminal networks do not utilize the IM problem to understand its dynamics. The proposed work introduces IM to investigate its role in criminal social networks and proposes an algorithm IM-C that finds the key influential nodes in criminal social networks. This work examines key influencers and their influence on criminal networks. Further, by introducing IM, this study inspects the role of IM in geographic impact, resilience and vulnerability in criminal networks and the influence of centralities-based key users. The experiments are conducted on six datasets, and the experimental results show that the proposed algorithm outperformed the benchmark algorithms. The findings of key users, their influence, geographic impact, resilience and vulnerability provide insights into disrupting criminal networks and designing targeted intervention strategies for crime reduction. Thus, through experiment results, this work demonstrates that IM in criminal networks provides an impactful novel approach to uncover insights such as identifying key influencers, understanding their impact, and assessing network resilience and vulnerability.

High utility itemsets (HUIs) mining is an emerging area of data mining which discovers sets of items generating a high profit from transactional datasets. In recent years, several algorithms have been proposed for this task. However, most of them do not consider the on-shelf time period of items and negative utility of items. High on-shelf utility itemset (HOUIs) mining is more difficult than traditional HUIs mining because it deals with on-shelf-based time period and negative utility of items. Moreover, most algorithms need minimum utility threshold ( min_util ) to find rules. However, specifying the appropriate min_util threshold is a difficult problem for users. A smaller min_util threshold may generate too many rules and a higher one may generate a few rules, which can degrade performance. To address these issues, a novel top-k HOUIs mining algorithm named TKOS ( T op- K high O n- S helf utility itemsets miner) is proposed which considers on-shelf time period and negative utility. TKOS presents a novel branch and bound-based strategy to raise the internal min_util threshold efficiently. It also presents two pruning strategies to speed up the mining process. In order to reduce the dataset scanning cost, we utilize transaction merging and dataset projection techniques. Extensive experiments have been conducted on real and synthetic datasets having various characteristics. Experimental results show that the proposed algorithm outperforms the state-of-the-art algorithms. The proposed algorithm is up to 42 times faster and uses up-to 19 times less memory compared to the state-of-the-art KOSHU. Moreover, the proposed algorithm has excellent scalability in terms of time periods and the number of transactions.

Influence maximization (IM) is the fundamental study of social network analysis. The IM problem finds the top k nodes that have maximum influence in the network. Most of the studies in IM focus on maximizing the number of activated nodes in the static social network. But in real life, social networks are dynamic in nature. This work addresses the diversification of activated nodes in the dynamic social network. This work proposes an objective function that maximizes the number of communities by utilizing bridge nodes. We also propose a diffusion model that considers the role of inactive nodes in influencing a node. We prove the submodularity, and monotonicity of the objective function under the proposed diffusion model. This work analyzes the impact of different ratios of bridge nodes in the seed set on real-world and synthetic datasets. Furthermore, we prove the NP-Hardness of the objective function under the proposed diffusion model. The experiments are conducted on various real-world and synthetic datasets with known and unknown community information. The proposed work experimentally shows that the objective function gives the maximum number of communities considering bridge nodes compared with the benchmark algorithms.

Online social networks are crucial in propagating information and exerting influence through word-of-mouth transmission. Influence maximization (IM) is the fundamental task in social network analysis to find the group of nodes that maximizes the influence in the social network. IM has different applications like viral marketing, campaigning, advertising, and so on. Literature has presented various algorithms based on different approaches to address the IM problem, including nature-inspired algorithms. Most of the work focuses on the static social network. The proposed work first employs nature-inspired Cuckoo Search Optimization to solve the IM problem in dynamic networks. The proposed algorithm applies the fuzzy-logic-based technique to optimize the nests. We also perform statistical tests to show the effectiveness of the proposed algorithm with the benchmark algorithms. The experimental results are performed on five datasets and compare the results with the state-of-the-art algorithms. The results show that the proposed algorithm gives better results than the nature-inspired state-of-the-art algorithms.

Summary Online social networks play a pivotal role in the propagation of information and influence as in the form of word‐of‐mouth spreading. The influence maximization (IM) problem is a fundamental problem to identify a small set of individuals, which have a maximal influence spread in the social network. Unfortunately, the IM problem is NP‐hard. It has been depicted that a hill‐climbing greedy approach gives a good approximation guarantee. However, it is inefficient to run on large‐scale social networks. In this paper, a global influence evaluation function is presented for the IM optimization problem. The global influence evaluation function provides a reliable expected diffusion value of influence spread under the traditional diffusion models. To optimize global influence evaluation function, an influence maximization algorithm based on social spider optimization (IM‐SSO) is presented. IM‐SSO redefines the representation and update rule of spider's vibration and performs random walk towards target vibration. The algorithm uses a jump away process to overcome the weakness of premature convergence. The experimental results on six real‐world social networks show that the proposed algorithm is more effective than the state‐of‐the‐art heuristics and more time‐efficient than CELF++, static greedy, and PSO with an approximate influence spread.

Abstract High‐utility itemset (HUI) mining is an important tasks during data mining. Recently, many algorithms have been proposed to discover HUIs. Most of the algorithms work only for itemsets with positive utility values. However, in the real world, items are found with both positive and negative utility values. To address this issue, we propose an algorithm named Efficient High‐utility Itemsets mining with Negative utility (EHIN) to find all HUIs with negative utility. EHIN utilises 2 new upper bounds for pruning, named revised subtree and revised local utility. To reduce dataset scans, the proposed algorithm uses transaction merging and dataset projection techniques. An array‐based utility‐counting technique is also utilised to calculate upper‐bound efficiently. EHIN utilises various properties and pruning strategies to mine HUIs with negative utility. The experimental results show that the proposed algorithm is 28 times faster, and it consumes up to 10 times less memory than the state‐of‐the‐art algorithm FHN. Moreover, a key advantage is that EHIN always performs better for dense datasets.