Influence maximization (IM), a central issue in optimizing information diffusion on social platforms, aims to spread posts or comments more widely, rapidly, and efficiently. Existing studies primarily focus on the positive effects of incorporating heuristic calculations in IM approaches. However, heuristic models fail to consider the potential enhancements that can be achieved through network representation learning techniques. Some recent work is keen to use representation learning to deal with IM issues. However, few in-depth studies have explored the existing challenges in IM representation learning, specifically regarding the role characteristics and role representations. This paper highlights the potential advantages of combining heuristic computing and role embedding to solve IM problems. First, the method introduces role granularity classification to effectively categorize users into three distinct roles: opinion leaders, structural holes and normal nodes. This classification enables a deeper understanding of the dynamics of users within the network. Second, a novel role-based network embedding (RbNE) algorithm is proposed. By leveraging the concept of node roles, RbNE captures the similarity between nodes, allowing for a more accurate representation of the network structure. Finally, a superior IM approach, named RbneIM, is recommended. RbneIM combines heuristic computing and role embedding to establish a fusion-enhanced IM solution, resulting in an improved influence analysis process. Exploratory outcomes on six social network datasets indicate that the proposed approach outperforms state-of-the-art seeding algorithms in terms of maximizing influence. This finding highlights the effectiveness and efficacy of the proposed method in achieving higher levels of influence within social networks. The code is available at https://github.com/baiyazi/IM2.

Influence maximization in social networks using role-based embedding

Identifying the influential and spreader nodes in complex networks solves many types of complex scientific problems. In social networks, identifying the influential individuals can be useful for structuring techniques that accelerate or hinder information propagation. Each node in the network has unique characteristics that reflect its importance. These characteristics are used by researchers to design many different centrality algorithms. Unfortunately, current survey papers categorize these algorithms into broad classes and do not draw distinguishable boundaries among the specific techniques adopted by them. This can result in misclassifying unrelated algorithms into the same analysis category. To overcome this, we introduce a methodology-based taxonomy for classifying the algorithms that identify top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> influential spreaders into hierarchically nested, specific, and fine-grained categories. We survey 184 papers and discuss their algorithms, which fall under 26 specific techniques. Our methodological taxonomy classifies the algorithms hierarchically into the following manner: Analysis type <inline-formula> <tex-math notation="LaTeX">$ \rightarrow $ </tex-math></inline-formula> analysis scope <inline-formula> <tex-math notation="LaTeX">$ \rightarrow $ </tex-math></inline-formula> analysis approach <inline-formula> <tex-math notation="LaTeX">$ \rightarrow $ </tex-math></inline-formula> analysis category <inline-formula> <tex-math notation="LaTeX">$ \rightarrow $ </tex-math></inline-formula> analysis sub-category <inline-formula> <tex-math notation="LaTeX">$ \rightarrow $ </tex-math></inline-formula> analysis specific technique. We introduce in this paper a comprehensive survey, review, and experimental evaluation of the recent and state-of-the-art algorithms that identify the top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> and influential spreader nodes in social networks.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09913983.pdf

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

The concept of lurking is becoming increasingly popular these days. Lurkers (also known as silent users) are members of Online Social Networks (OSNs) who intentionally choose to observe rather than engage. There is a growing need to delurk these users and make use of their skills in computer science. In this paper, we discuss the concept of lurkers in online social networks, as well as the different categories of delurking mechanisms, including influence maximization. We first introduce the notion of lurking and the importance of transforming these users into active ones. Then, we explore the reasons behind this phenomenon and the motives that make people act in this way in OSNs. In the last chapter, we present ways of delurking, focusing on methods such as rewarding mechanisms and influence maximization.

Delurking and Influence Maximization in Online Social Networks

Research on Influence Maximization has gained a lot of attention in the recent past. Part of the reason for this is that influence maximization has applications in commercially attractive areas such as word of mouth marketing. A majority of works in influence maximization have relied on information diffusion models that largely ignore the structural properties of the social network. The problem with this is that important attributes of user relationships which are necessary for approximating influence maximization are ignored. Parameters such as Homophily and Topological Overlap are crucial determinants of the level of influence that a user enjoys in the network. This work approximates global influence power of a user by first, considering user interactions, homophily and topological overlap as determinants of node to node relationship strength on a dynamic social graph. Then secondly, computes a global score of influence for each user. We then apply a novel algorithm that approximates influence spread for each influential user. The seed set is built by identifying the most influential users at specific time instances as the social graph evolves.

Influence Maximization: Incorporating Homophily and Topological Overlap

In today’s online society as social media has become an integral part of our daily lives, lurking is transformed to a well-grounded phenomenon. Silent users or otherwise called lurkers are users of Online Social Networks (OSNs) who intentionally decide to not make their presence visible to other users by remaining silent, keeping their online identity as a secret and observe, rather than visibly participate. They do not share any kind of information or preferences with the other members of the community. The goal of this study is to raise awareness regarding the common phenomenon of lurking, examine the lurking phenomenon and reveal ways of delurking this kind of users in social media platforms. Therefore, this study provides responses on how by unveiling the lurking phenomenon knowledge will be provided in different scientific domains such as social science, human-computer interaction, and computer science fields. 

Importance and Effectiveness of Delurking

A majority of influence maximization models in social networks in literature are based on a seminal work by Kempe et al., in which two classic influence models were proposed i.e Linear Threshold Model and Independent Cascade Model. However, these two models use assumed values to model influence and influence propagation in social networks. This may lead to inaccurate approximation of influence. In this work, we model influence from actual social actions among members of a social network through a proposed algorithm - Selective Breadth First Traversal - that efficiently generates an optimal seed set for influence maximization. Experimental results on real data show that our approach provides an improvement over a number of traditional influence maximization algorithms.

Influence maximization through user interaction modeling

Social networks, such as Twitter and Facebook have recently emerged to play key roles in crucial aspects of our lives. In this regard, the question of how to model the relationships between users on the online interactive platform has become important because it forms a basis for abstracting a social network using social network data. Furthermore, user to user relationship definition gives a more convinient way to represent tie strength between users. The analysis of the tie strength provides a better way to gauge how important users are to one another.In this paper, we conduct a survey on the various approaches that researchers of social network analysis have applied in modelling relationships between users. Several approaches have been discussed and we have pointed out the challenges and benefits associated with some of the approaches. We have shown how the individual relationship models fit into the bigger models of social influence, which is a major subject of research on social networks. Finally we have recommended future research for some areas that do not seem to have been addressed yet.

Modeling Online User Relationships: A Review

Influence on the social network platform has become an interesting research area. A typical social network influencer is known by the amount of reactions that he/she attracts based on the posts made. The amount of reactions received is usually a reflection of the overall visibility of the influencer on the social network arena. This visibility has attracted commercial benefits to such influencers through word of mouth marketing, political endorsements as well as ambassadorial appointments. However, existing methods in literature that are used to scientifically quantify the amount of social influence that can be attributed to an influencer, tend to lump the amount of social influence in a single basket labelled either as positive or negative influence. The effect is a binary classification of influencers as either negative or positive. In this article, we make the case that this is not necessarily accurate. The reason being that in a collection of comments that a post from an influencer attracts, it it seldom that all the comments would be absolutely positive or negative. There is a fuzzy space defined by comments that do not necessarily belong to either of these categories. In this work, we have used data harvested from Facebook comments and grouped into four different categories - those in full support of the opinion expressed, those in full opposition of the opinion, those that somewhat support and those that somewhat oppose the opinion expressed. Results have shown that the fuzzy space created by the comments in between the full support and full opposition negates the assumption that an influencer can be a positive or negative influencer just because the number of positive or negative comments are the majority.

Social Network Influence: Making the Case for Semantic Analysis

The banking industry has seen a revolutionary moment in adopting mobile banking technology in the last few years. The technology has contributed to many customers keeping off the banking hall for basic and routine transactions. This has largely been attributed to the convenience of mobile banking, including time management and increased privacy. However, there are still times when a bank conducts large-scale service provision for many people. For example, a payment agency contracted by the government to disburse funds to a section of the citizens may require that money be channeled only through a particular bank. If the period for such a requirement is limited, there is likely to be pressure on both the bank and the would-be customers because of long queues. For cases like that, there is a need to simulate possible scenarios and plan to avoid delays and frustrations for both parties. This work proposes a queue simulation model that can be used to forecast the number of bank staff that can be deployed for such a scenario vis a vis the expected number of customers seeking service. Results have shown that the simulation model can help the bank management make optimal choices that will avoid waste in the form of human or financial resources.

https://doi.org/10.7763/ijmo.2023.v13.826

A Simulation of the Optimal Personnel Demand for Banking Hall Services

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Papers

Influence maximization through user interaction modeling

Modeling Online User Relationships: A Review

Social Network Influence: Making the Case for Semantic Analysis

A Simulation of the Optimal Personnel Demand for Banking Hall Services