Multi-objective community detection applied to social and COVID-19 constructed networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonCommunity Detection plays an integral part in network analysis, as it facilitates understanding the structures and functional characteristics of the network. Communities organize real-world networks into densely connected groups of nodes. This thesis provides a critical analysis of the Community Detection and highlights the main areas including algorithms, evaluation metrics, applications, and datasets in social networks. After defining the research gap, this thesis proposes two Attribute-Based Label Propagation algorithms that maximizes both Modularity and homogeneity. Homogeneity is considered as an objective function one time, and as a constraint another time. To better capture the homogeneity of real-world networks, a new Penalized Homogeneity degree (PHd) is proposed, that can be easily personalized based on the network characteristics. For the first time, COVID-19 tracing data are utilized to form two dataset networks: one is based on the virus transition between the world countries. While the second dataset is an attributed network based on the virus transition among the contact-tracing in the Kingdom of Bahrain. This type of networks that is concerned in tracking a disease was not formed based on COVID-19 virus and has never been studied as a community detection problem. The proposed datasets are validated and tested in several experiments. The proposed Penalized Homogeneity measure is personalized and used to evaluate the proposed attributed network. Extensive experiments and analysis are carried out to evaluate the proposed methods and benchmark the results with other well-known algorithms. The results are compared in terms of Modularity, proposed PHd, and accuracy measures. The proposed methods have achieved maximum performance among other methods, with 26.6% better performance in Modularity, and 33.96% in PHd on the proposed dataset, as well as noteworthy results on benchmarking datasets with improvement in Modularity measures of 7.24%, and 4.96% respectively, and proposed PHd values 27% and 81.9%

Big Networks: Analysis and Optimal Control

The study of networks has seen a tremendous breed of researches due to the explosive spectrum of practical problems that involve networks as the access point. Those problems widely range from detecting functionally correlated proteins in biology to finding people to give discounts and gain maximum popularity of a product in economics. Thus, understanding and further being able to manipulate/control the development and evolution of the networks become critical tasks for network scientists. Despite the vast research effort putting towards these studies, the present state-of-the-arts largely either lack of high quality solutions or require excessive amount of time in real-world `Big Data\u27 requirement. This research aims at affirmatively boosting the modern algorithmic efficiency to approach practical requirements. That is developing a ground-breaking class of algorithms that provide simultaneously both provably good solution qualities and low time and space complexities. Specifically, I target the important yet challenging problems in the three main areas: Information Diffusion: Analyzing and maximizing the influence in networks and extending results for different variations of the problems. Community Detection: Finding communities from multiple sources of information. Security and Privacy: Assessing organization vulnerability under targeted-cyber attacks via social networks

Seeding as Part of the Marketing Mix:Word-of-Mouth Program Interactions for Fast-Moving Consumer Goods

Seeded marketing campaigns (SMCs) have become part of the marketing mix for many fast-moving consumer goods (FMCG) companies. In addition to making large investments in advertising and sales promotions, these firms now encourage seed agents or microinfluencers to discuss brands with friends and acquaintances to create further value. It is thus critical to understand how an FMCG seeding program interacts with traditional marketing tools when estimating the effectiveness of such efforts. However, the issue is still underexplored. The authors present the first empirical analysis of this question using a rich data set collected on four brands from various European FMCG markets. They combine advertising and sales promotion data from FMCG brand managers with sales and retail variables from market research companies as well as firm-created word-of-mouth variables from SMC agencies. The authors analyze the data using several approaches, confronting challenges of endogeneity and multicollinearity. They consistently find that firm-created word of mouth through SMC programs interacts negatively with all tested forms of advertising but positively with promotional activities. This phenomenon has significant implications for understanding the utility of SMCs and how they should be managed. The analysis implies that SMCs may increase total sales by approximately 3%-18% throughout the campaigns

Combating Fake News on Social Media: A Framework, Review, and Future Opportunities

Social media platforms facilitate the sharing of a vast magnitude of information in split seconds among users. However, some false information is also widely spread, generally referred to as “fake news”. This can have major negative impacts on individuals and societies. Unfortunately, people are often not able to correctly identify fake news from truth. Therefore, there is an urgent need to find effective mechanisms to fight fake news on social media. To this end, this paper adapts the Straub Model of Security Action Cycle to the context of combating fake news on social media. It uses the adapted framework to classify the vast literature on fake news to action cycle phases (i.e., deterrence, prevention, detection, and mitigation/remedy). Based on a systematic and inter-disciplinary review of the relevant literature, we analyze the status and challenges in each stage of combating fake news, followed by introducing future research directions. These efforts allow the development of a holistic view of the research frontier on fighting fake news online. We conclude that this is a multidisciplinary issue; and as such, a collaborative effort from different fields is needed to effectively address this problem

Data Mining

The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining

Social Media Influencers- A Review of Operations Management Literature

This literature review provides a comprehensive survey of research on Social Media Influencers (SMIs) across the fields of SMIs in marketing, seeding strategies, influence maximization and applications of SMIs in society. Specifically, we focus on examining the methods employed by researchers to reach their conclusions. Through our analysis, we identify opportunities for future research that align with emerging areas and unexplored territories related to theory, context, and methodology. This approach offers a fresh perspective on existing research, paving the way for more effective and impactful studies in the future. Additionally, gaining a deeper understanding of the underlying principles and methodologies of these concepts enables more informed decision-making when implementing these strategie

INDIGO: a generalized model and framework for performance prediction of data dissemination

According to recent studies, an enormous rise in location-based mobile services is expected in future. People are interested in getting and acting on the localized information retrieved from their vicinity like local events, shopping offers, local food, etc. These studies also suggested that local businesses intend to maximize the reach of their localized offers/advertisements by pushing them to the maxi- mum number of interested people. The scope of such localized services can be augmented by leveraging the capabilities of smartphones through the dissemination of such information to other interested people. To enable local businesses (or publishers) of localized services to take in- formed decision and assess the performance of their dissemination-based localized services in advance, we need to predict the performance of data dissemination in complex real-world scenarios. Some of the questions relevant to publishers could be the maximum time required to disseminate information, best relays to maximize information dissemination etc. This thesis addresses these questions and provides a solution called INDIGO that enables the prediction of data dissemination performance based on the availability of physical and social proximity information among people by collectively considering different real-world aspects of data dissemination process. INDIGO empowers publishers to assess the performance of their localized dissemination based services in advance both in physical as well as the online social world. It provides a solution called INDIGO–Physical for the cases where physical proximity plays the fundamental role and enables the tighter prediction of data dissemination time and prediction of best relays under real-world mobility, communication and data dissemination strategy aspects. Further, this thesis also contributes in providing the performance prediction of data dissemination in large-scale online social networks where the social proximity is prominent using INDIGO–OSN part of the INDIGO framework under different real-world dissemination aspects like heterogeneous activity of users, type of information that needs to be disseminated, friendship ties and the content of the published online activities. INDIGO is the first work that provides a set of solutions and enables publishers to predict the performance of their localized dissemination based services based on the availability of physical and social proximity information among people and different real-world aspects of data dissemination process in both physical and online social networks. INDIGO outperforms the existing works for physical proximity by providing 5 times tighter upper bound of data dissemination time under real-world data dissemination aspects. Further, for social proximity, INDIGO is able to predict the data dissemination with 90% accuracy and differently, from other works, it also provides the trade-off between high prediction accuracy and privacy by introducing the feature planes from an online social networks

Elections in digital times: a guide for electoral practitioners

Strengthening democracy and electoral processes in the era of social media and Artificial Intelligence Democracy requires free, periodic, transparent, and inclusive elections. Freedom of expression, freedom of the press, and the right to political participation are also critical to societies ruled by the respect of human rights. In today’s rapidly evolving digital environment, opportunities for communication between citizens, politicians and political parties are unprecedented –– with information related to elections flowing faster and easier than ever, coupled with expanded opportunities for its verification and correction by a growing number of stakeholders. However, with billions of human beings connected, and disinformation and misinformation circulating unhinged around the networks, democratic processes and access to reliable information are at risk. With an estimated 56.8% of the world’s population active on social media and an estimate of 4 billion eligible voters, the ubiquity of social networks and the impact of Artificial Intelligence can intentionally or unintentionally undermine electoral processes, thereby delegitimizing democracies worldwide. In this context, all actors involved in electoral processes have an essential role to play. Electoral management bodies, electoral practitioners, the media, voters, political parties, and civil society organizations must understand the scope and impact of social media and Artificial Intelligence in the electoral cycle. They also need to have access to the tools to identify who instigates and spreads disinformation and misinformation, and the tools and strategies to combat it. This handbook aims to be a toolbox that helps better understand the current scenario and share experiences of good practices in different electoral settings and equip electoral practitioners and other key actors from all over the world to ensure the credibility of the democratic system in times of profound transformations