Relationships In Online Social Networks Research Articles

Hypergraph-Based Influence Maximization in Online Social Networks

Influence maximization in online social networks is used to select a set of influential seed nodes to maximize the influence spread under a given diffusion model. However, most existing proposals have huge computational costs and only consider the dyadic influence relationship between two nodes, ignoring the higher-order influence relationships among multiple nodes. It limits the applicability and accuracy of existing influence diffusion models in real complex online social networks. To this end, in this paper, we present a novel information diffusion model by introducing hypergraph theory to determine the most influential nodes by jointly considering adjacent influence and higher-order influence relationships to improve diffusion efficiency. We mathematically formulate the influence maximization problem under higher-order influence relationships in online social networks. We further propose a hypergraph sampling greedy algorithm (HSGA) to effectively select the most influential seed nodes. In the HSGA, a random walk-based influence diffusion method and a Monte Carlo-based influence approximation method are devised to achieve fast approximation and calculation of node influences. We conduct simulation experiments on six real datasets for performance evaluations. Simulation results demonstrate the effectiveness and efficiency of the HSGA, and the HSGA has a lower computational cost and higher seed selection accuracy than comparison mechanisms.

Using Bayesian networks with hidden variables for identifying trustworthy users in social networks

The popularity and broad accessibility of online social networks (OSNs) have facilitated effective communication among people, but such networks also pose potential risks that should not be ignored. Interaction through OSNs is complex and can be unsafe, as individuals can be contacted by strangers at any time. This makes the notion of trust a crucial issue in the use of OSNs. However, compared with decision-making processes associated with whether to trust a stranger encountered in everyday life, this task is more difficult to address with regard to OSNs due to the lack of face-to-face communication and prior knowledge between people. In this article, trust evaluation is formalised as a classification problem. We demonstrate how user profiles and historical records can be organised into a logical structure based on Bayesian networks to recognise the trustworthy people without the need to build trust relationships in OSNs. This is possible when a more detailed description of features denoted by hidden variables is considered. We compare the performance of our method with those of six other machine learning methods using Facebook and Twitter datasets, and our results show that our method achieves higher values in accuracy, recall and F1 score.

Mining hidden non-redundant causal relationships in online social networks

Causal discovery is crucial to obtain a deep understanding of the actual mechanism behind the online social network, e.g., identifying the influential individuals and understanding the interaction among user behavior sequences. However, detecting causal directions and pruning causal redundancy of online social networks are still the great challenge of existing research. This paper proposed a constraint-based approach, minimal causal network (MCN), to mine hidden non-redundant causal relationships behind user behavior sequences. Under the MCN, the transfer entropy with the adaptive causal time lag is used to detect causal directions and find causal time lags, while a permutation-based significance test is proposed to prune redundant edges. Experiments on simulated data verify the effectiveness of our proposed method. We also apply our approach to real-world data from Sina Weibo and reveal some interesting discoveries.

A novel pricing scheme for managing virtual energy communities and promoting behavioral change towards energy efficiency

The harmonization between the variable rate of energy production in the era of massive renewable energy penetration is a major challenge in an open, competitive and resilient electricity market. As a result, there is an increasing need for modern pricing schemes, which will effectively incentivize willing users to modify their energy consumption pattern to meet this objective. Current energy pricing schemes (e.g. real time pricing) treat all users the same, and do not adequately compensate for behavioral changes, thus mitigating the behavioral change dynamics. In this paper, we propose a Community Real Time Pricing (CRTP) scheme together with an Energy Community Formation Algorithm (ECFA), where users are clustered in Virtual Energy Communities (VECs) according to: (i) their level of flexibility in modifying their Energy Consumption Curve (ECC), and (ii) their relationships in Online Social Networks (OSNs), modelling peer-pressure capabilities. We show that CRTP with ECFA can simultaneously achieve considerable reduction in the system’s energy cost and greater aggregated users’ welfare than with the state-of-the-art real time pricing. CRTP–ECFA adopts a truly fair pricing policy, as each user is rewarded exactly according to his/her individual contribution in reducing system costs, thus promoting the desired behavioral change.

Research on China’s city network based on users’ friend relationships in online social networks: a case study of Sina Weibo

Cities no longer develop independently but exist in a large city network in the globalization era because of the rapid development of information and communication technologies. Instead of attribute data, relational data has been widely recognized as an improved tool to analyze the importance of a city in a city network. This study attempts to analyze China’s city network based on users’ friend relationships in Sina Weibo, the most popular online social networking service in China. By collecting friend relationship data from 20 users located in the selected 51 cities, a city network is built based on connections in the virtual world. Findings show that although microblog users build friend relationships with other users located almost everywhere, significant variations exist regarding the closeness of such connections. In particular, a limited number of cities accumulate the majority of top connections; the limited number indicates the significant power of these key cities in the city network. These top connections are also highly related to cities located in Eastern China. The regression model demonstrates that a city with a higher of per capita GDP exerts a powerful influence in the city network. Compared with a prefecture-level city, a municipality directly under central government is considerably powerful. Therefore, the importance of a city in the real world has significant influence on the power of that city in the city network based on online social networking connections.

Assessment of subjective influence and trust with an online social network game

The deduction of influence and trust between two individuals only from objective data in online social networks (OSNs) is a rather vague approach. Subjective assessments via surveys produce better results, but are harder to conduct considering the vast amount of friendships of OSN users. This work presents a framework for personalized surveys on relationships in OSNs, which follows a gamification approach. A Facebook game was developed, which was used to subjectively assess social influence and interpersonal trust based on models from psychology. The results show that it is possible to obtain subjective opinions and (limited) objective data about relationships with an OSN game. Also an implicit assessment of influence and trust with subcategory questions is feasible in this case.

Organization Mining Using Online Social Networks

Complementing the formal organizational structure of a business are the informal connections among employees. These relationships help identify knowledge hubs, working groups, and shortcuts through the organizational structure. They carry valuable information on how a company functions de facto. In the past, eliciting the informal social networks within an organization was challenging; today they are reflected by friendship relationships in online social networks. In this paper we analyze several commercial organizations by mining data which their employees have exposed on Facebook, LinkedIn, and other publicly available sources. Using a web crawler designed for this purpose, we extract a network of informal social relationships among employees of targeted organizations. Our results show that it is possible to identify leadership roles within the organization solely by using centrality analysis and machine learning techniques applied to the informal relationship network structure. Valuable non-trivial insights can also be gained by clustering an organization’s social network and gathering publicly available information on the employees within each cluster. Knowledge of the network of informal relationships may be a major asset or might be a significant threat to the underlying organization.

Reciprocal versus parasocial relationships in online social networks

Many online social networks are fundamentally directed, i.e., they consist of both reciprocal edges (i.e., edges that have already been linked back) and parasocial edges (i.e., edges that have not been linked back). Thus, understanding the structures and evolutions of reciprocal edges and parasocial ones, exploring the factors that influence parasocial edges to become reciprocal ones, and predicting whether a parasocial edge will turn into a reciprocal one are basic research problems. However, there have been few systematic studies about such problems. In this paper, we bridge this gap using a novel large-scale Google+ dataset (available at http://www.cs.berkeley.edu/~stevgong/dataset.html/ ) crawled by ourselves as well as one publicly available social network dataset. First, we compare the structures and evolutions of reciprocal edges and those of parasocial edges. For instance, we find that reciprocal edges are more likely to connect users with similar degrees while parasocial edges are more likely to link ordinary users (e.g., users with low degrees) and popular users (e.g., celebrities). However, the impacts of reciprocal edges linking ordinary and popular users on the network structures increase slowly as the social networks evolve. Second, we observe that factors including user behaviors, node attributes, and edge attributes all have significant impacts on the formation of reciprocal edges. Third, in contrast to previous studies that treat reciprocal edge prediction as either a supervised or a semi-supervised learning problem, we identify that reciprocal edge prediction is better modeled as an outlier detection problem. Finally, we perform extensive evaluations with the two datasets, and we show that our proposal outperforms previous reciprocal edge prediction approaches.