Heterogeneous Influence Maximization Through Community Detection in Social Networks

The problem of identifying a seed set composed of K nodes that increase influence spread over a social network is known as influence maximization (IM). Past works showed this problem to be NP-hard and an optimal solution to this problem using greedy algorithms achieved only 63% of spread. However, this approach is expensive and suffered from performance issues like high computational cost. Furthermore, in a network with communities, IM spread is not always certain. In this paper, heterogeneous influence maximization through community detection (HIMCD) algorithm is proposed. This approach addresses initial seed nodes selection in communities using various centrality measures, and these seed nodes act as sources for influence spread. A parallel influence maximization is applied with the aid of seed node set contained in each group. In this approach, graph is partitioned and IM computations are done in a distributed manner. Extensive experiments with two real-world datasets reveals that HCDIM achieves substantial performance improvement over state-of-the-art techniques.

Hashtags Influence Maximization: Choosing the most Influential Hashtags on Instagram

This study aims to find influential hashtags using Influence Maximization (IM). The IM approach was implemented using hashtags network collected from Instagram. This study can help business or ordinary users to choose the most engaging hashtags for posting, as opposed to selecting influencers, which was widely studied using the IM approach. The network was build based on the hashtags co-appearance frequency. Three IM algorithms, i.e. SSA, DSSA, and IMM, were simulated under the IC and LT models. The algorithms were compared against TopUsage, which is the top hashtags based on the usage count. The IM algorithms have a similar performance with TopUsage in influence spread, which shows that IM can adapt to the hashtags network. However, the IM algorithms produced better hashtags based on the UER (unique engagement rate) metric. The best UER performance was shown by DSSA under the LT model, where it outperformed TopUsage by 17.23%. In the hashtags categorization scenario, DSSA-LT outperformed the UER of TopUsage by up to 6.87%. This categorization is more useful in a practical scenario, to find only relevant hashtags for posting. The hashtags generated by DSSA-LT are about 30-35% different from TopUsage.

Introduction

The introduction situates the book within the broader discourse of work on the Roman conquest of Italy. It begins by discussing the theoretical underpinnings and methodological considerations of the work. After a historiographical discussion of “Romanization studies,” it mentions three models that will be drawn on in the work (those of Mattingly, Barrett, and Terrenato). It then discusses the importance of the spread of Hellenistic culture throughout Italy for studies of the Roman conquest. Next, it examines recent regional studies of the Roman conquest of Italy, particularly in central and southern Italy. It brings up three key questions that will be addressed in the work: How did Larinum’s participation in the broader Hellenistic koiné contribute to its integration into the Roman state? What forms of Roman influence spread to Larinum during the period in question and how did they arrive there? And, in what ways do the changes in Larinum’s material record reflect broader cultural developments both at the site and within its territory? It makes the case for Larinum’s being a prime candidate for this type of study by laying out the available evidence for the creation of a site biography before ending with an overview of the main argument of the book.

An Enigma: The Legend of Saint Benedict

This chapter introduces a few key features of Saint Benedict’s life and death, considering the historical process by which he became known as Monte Cassino’s ‘animus’ and ‘anchor’ – the abbey’s spirit and foundation. This line of enquiry means asking how his influence spread, shaped, and fostered the abbey’s reputation as a centre of spiritual, religious, and intellectual culture. It also means considering the contested narratives surrounding his possible translation, in addition to the many discoveries of his relics and their contribution to the entrenched historiography.

Adaptive Influence Maximization

Influence maximization problem attempts to find a small subset of nodes that makes the expected influence spread maximized, which has been researched intensively before. They all assumed that each user in the seed set we select is activated successfully and then spread the influence. However, in the real scenario, not all users in the seed set are willing to be an influencer. Based on that, we consider each user associated with a probability with which we can activate her as a seed, and we can attempt to activate her many times. In this article, we study the adaptive influence maximization with multiple activations (Adaptive-IMMA) problem, where we select a node in each iteration, observe whether she accepts to be a seed, if yes, wait to observe the influence diffusion process; if no, we can attempt to activate her again with a higher cost or select another node as a seed. We model the multiple activations mathematically and define it on the domain of integer lattice. We propose a new concept, adaptive dr-submodularity, and show our Adaptive-IMMA is the problem that maximizing an adaptive monotone and dr-submodular function under the expected knapsack constraint. Adaptive dr-submodular maximization problem is never covered by any existing studies. Thus, we summarize its properties and study its approximability comprehensively, which is a non-trivial generalization of existing analysis about adaptive submodularity. Besides, to overcome the difficulty to estimate the expected influence spread, we combine our adaptive greedy policy with sampling techniques without losing the approximation ratio but reducing the time complexity. Finally, we conduct experiments on several real datasets to evaluate the effectiveness and efficiency of our proposed policies.

Parallel Greedy Algorithm to Multiple Influence Maximization in Social Network

Influence Maximization (IM) problem is to select influential users to maximize the influence spread, which plays an important role in many real-world applications such as product recommendation, epidemic control, and network monitoring. Nowadays multiple kinds of information can propagate in online social networks simultaneously, but current literature seldom discuss about this phenomenon. Accordingly, in this article, we propose Multiple Influence Maximization (MIM) problem where multiple information can propagate in a single network with different propagation probabilities. The goal of MIM problems is to maximize the overall accumulative influence spreads of different information with the limit of seed budget . To solve MIM problems, we first propose a greedy framework to solve MIM problems which maintains an -approximate ratio. We further propose parallel algorithms based on semaphores, an inter-thread communication mechanism, which significantly improves our algorithms efficiency. Then we conduct experiments for our framework using complex social network datasets with 12k, 154k, 317k, and 1.1m nodes, and the experimental results show that our greedy framework outperforms other heuristic algorithms greatly for large influence spread and parallelization of algorithms reduces running time observably with acceptable memory overhead.

RLIM: Representation Learning Method for Influence Maximization in social networks

A core issue in influence propagation is influence maximization, which aims to find a group of nodes under a specific information diffusion model and maximize the final influence of this group of nodes. The limitation of the existing researches is that they excessively depend on the information diffusion model and randomly set the propagation ability (probability). Therefore, most of the algorithms for solving the influence maximization problem are basically difficult to expand in large social networks. Another challenge is that fewer researchers have paid attention to the problem of the large difference between the estimated influence spread and the actual influence spread. A measure to solve the influence maximization problem is applying advanced neural network architecture also represents learning method. Based on this idea, the paper proposes Representation Learning for Influence Maximization (RLIM) algorithm. The premise of this algorithm is to construct the influence cascade of each source node. The key is to adopt neural network architecture to realize the prediction of propagation ability. The purpose is to apply the propagation ability to the influence maximization problem by representation learning. Furthermore, the results of the experiments show that RLIM algorithm has greater diffusion ability than the state-of-the-art algorithms on different online social network data sets, and the diffusion of information is more accurate.

Realistic influence maximization based on followers score and engagement grade on instagram

In recent years, the emergence of social media influencers attracts the study of a realistic influence maximization (IM) technique. The theoretical performance of IM has become matured. However, it is not enough since IM has to be implemented in a social media environment. Realistic IM algorithms and diffusion models have been proposed, such as the addition of user factors or a learning agent. However, most studies still relied on the influence spread benchmark, which makes the usefulness questionable. This research is among the first IM study using Instagram data. In this study, two diffusion models are proposed, which are based on the original IC and LT models, with the addition of the engagement grade (EG) factor. An algorithm called IMFS (IM with followers score) is proposed to accommodate the new models as well as IC and LT. In addition, realistic benchmark methods are proposed, namely the average engagement of the activated users, and the overlapping between post likers and activated users. The result shows that the proposed models are 2-3x more realistic if compared to IC and LT.