Effects of Random Errors on Graph Convolutional Networks

The use of Graph Convolutional Networks (GCN) has been an emerging trend in the network science research community. While GCN achieves excellent performance in several tasks, there exists an open issue in applying GCN to real-world applications. The issue is the effects of network errors on GCN. Since real-world network data contain several types of noises and errors, GCN is desirable to be less affected by such errors. However, the effects have not been sufficiently evaluated before. In this paper, we analyze the effects of random errors on GCN through extensive experiments. The results show that the node classification accuracy of GCN is decreased only 5% even when 50% of the edges are randomly increased or decreased. Moreover, in terms of false labels, the accuracy of node classification is decreased only 10% even when 20% of the labels are changed

A Survey of Social Network Analysis Techniques and their Applications to Socially Aware Networking

Socially aware networking is an emerging research field that aims to improve the current networking technologies and realize novel network services by applying social network analysis (SNA) techniques. Conducting socially aware networking studies requires knowledge of both SNA and communication networking, but it is not easy for communication networking researchers who are unfamiliar with SNA to obtain comprehensive knowledge of SNA due to its interdisciplinary nature. This paper therefore aims to fill the knowledge gap for networking researchers who are interested in socially aware networking but are not familiar with SNA. This paper surveys three types of important SNA techniques for socially aware networking: identification of influential nodes, link prediction, and community detection. Then, this paper introduces how SNA techniques are used in socially aware networking and discusses research trends in socially aware networking

Effectiveness of Link Prediction for Face-to-Face Behavioral Networks

Research on link prediction for social networks has been actively pursued. In link prediction for a given social network obtained from time-windowed observation, new link formation in the network is predicted from the topology of the obtained network. In contrast, recent advances in sensing technology have made it possible to obtain face-to-face behavioral networks, which are social networks representing face-to-face interactions among people. However, the effectiveness of link prediction techniques for face-to-face behavioral networks has not yet been explored in depth. To clarify this point, here we investigate the accuracy of conventional link prediction techniques for networks obtained from the history of face-to-face interactions among participants at an academic conference. Our findings were (1) that conventional link prediction techniques predict new link formation with a precision of 0.30–0.45 and a recall of 0.10–0.20, (2) that prolonged observation of social networks often degrades the prediction accuracy, (3) that the proposed decaying weight method leads to higher prediction accuracy than can be achieved by observing all records of communication and simply using them unmodified, and (4) that the prediction accuracy for face-to-face behavioral networks is relatively high compared to that for non-social networks, but not as high as for other types of social networks

Identifying influencers from sampled social networks

Identifying influencers who can spread information to many other individuals from a social network is a fundamental research task in the network science research field. Several measures for identifying influencers have been proposed, and the effectiveness of these influence measures has been evaluated for the case where the complete social network structure is known. However, it is difficult in practice to obtain the complete structure of a social network because of missing data, false data, or node/link sampling from the social network. In this paper, we investigate the effects of node sampling from a social network on the effectiveness of influence measures at identifying influencers. Our experimental results show that the negative effect of biased sampling, such as sample edge count, on the identification of influencers is generally small. For social media networks, we can identify influencers whose influence is comparable with that of those identified from the complete social networks by sampling only 10%–30% of the networks. Moreover, our results also suggest the possible benefit of network sampling in the identification of influencers. Our results show that, for some networks, nodes with higher influence can be discovered from sampled social networks than from complete social networks

On the relation between message sentiment and its virality on social media

We investigate the relation between the sentiment of a message on social media and its virality, defined as the volume and speed of message diffusion. We analyze 4.1 million messages (tweets) obtained from Twitter. Although factors affecting message diffusion on social media have been studied previously, we focus on message sentiment and reveal how the polarity of message sentiment affects its virality. The virality of a message is characterized by the number of message repostings (retweets) and the time elapsed from the original posting of a message to its Nth reposting (N-retweet time). Through extensive analysis using the 4.1 million tweets and their retweets in 1 week, we discover that negative messages are likely to be reposted more rapidly and frequently than positive and neutral messages. Specifically, the reposting volume of negative messages is 20–60% higher than that of positive and neutral messages, and negative messages spread 25% faster than positive and neutral messages when the diffusion volume is quite high. We also perform longitudinal analysis of message diffusion observed over 1 year and find that recurrent diffusion of negative messages is less frequent than that of positive and neutral messages. Moreover, we present a simple message diffusion model that can reproduce the characteristics of message diffusion observed in this paper

An Accurate Graph Generative Model with Tunable Features

A graph is a very common and powerful data structure used for modeling communication and social networks. Models that generate graphs with arbitrary features are important basic technologies in repeated simulations of networks and prediction of topology changes. Although existing generative models for graphs are useful for providing graphs similar to real-world graphs, graph generation models with tunable features have been less explored in the field. Previously, we have proposed GraphTune, a generative model for graphs that continuously tune specific graph features of generated graphs while maintaining most of the features of a given graph dataset. However, the tuning accuracy of graph features in GraphTune has not been sufficient for practical applications. In this paper, we propose a method to improve the accuracy of GraphTune by adding a new mechanism to feed back errors of graph features of generated graphs and by training them alternately and independently. Experiments on a real-world graph dataset showed that the features in the generated graphs are accurately tuned compared with conventional models.Comment: This paper was presented at the 32nd International Conference on Computer Communications and Networks (ICCCN 2023) Poster Trac

A Survey on Modeling of Human States in Communication Behavior

The Technical Committee on Communication BehaviorEngineering addresses the research question How do we construct a com-munication network system that includes users?. The growth in highlyfunctional networks and terminals has brought about greater diversity inusers\u27 lifestyles and freed people from the restrictions of time and place.Under this situation, the similarities of human behavior cause traffic aggre-gation and generate new problems in terms of the stabilization of networkservice quality. This paper summarizes previous studies relevant to com-munication behavior from a multidisciplinary perspective and discusses theresearch approach adopted by the Technical Committee on CommunicationBehavior Engineering

Robustness of Networks with Skewed Degree Distributions under Strategic Node Protection

Previous studies on the robustness of networks against intentional attacks have suggested that protecting a small fraction of important nodes in a network significantly improves its robustness. In this paper, we analyze the robustness of networks under several strategic node protection schemes. Strategic node protection schemes select a small fraction of nodes as important nodes, using a network measuresuch as node centrality, and protect the important nodes to prevent them from being removed by intentional attacks. Our simulation results indicate that (1) strategic node protection significantly improves the robustness of networks with skewed degree distributions, (2) the efficiency of strategic node protection schemes is affected by the strength of community structure of the network being protected, and (3) strategic node protection based on betweenness centrality can effectively improve the robustness of networks regardless of the strength of community structure.ADMNET 2016: The 4th International Workshop on Architecture, Design, Deployment and Management of Networks and Applications(COMPSAC 2016: The 40th IEEE Computer Society International Conference on Computers, Software & Applications)Place: Atlanta, Georgia, USADate: June 10-14, 201

Estimating Influence of Social Media Users from Sampled Social Networks

Several indices for estimating the influence of social media users have been proposed. Most such indices are obtained from the topological structure of a social network that represents relations among social media users. However, several errors are typically contained in such social network structures because of missing data, false data, or poor node/link sampling from the social network. In this paper, we investigate the effects of node sampling from a social network on the effectiveness of indices for estimating the influence of social media users. We compare the estimated influence of users, as obtained from a sampled social network, with their actual influence. Our experimental results show that using biased sampling methods, such as sample edge count, is a more effective approach than random sampling for estimating user influence, and that the use of random sampling to obtain the structure of a social network significantly affects the effectiveness of indices for estimating user influence, which may make indices useless.ASONAM 2016 : The 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningPlace: San Francisco, CA, USADate: Aug 18, 2016 - Aug 21, 201

Empirical Analysis of the Relation between Community Structure and Cascading Retweet Diffusion

Social networks have community structure, in which the network is composed of highly clustered subnetworks (communities) with sparse links between them. Such community structure is expected to affect information diffusion among individuals. This paper empirically investigates how the community structure of a social network among Twitter users affects cascading diffusion of retweets among them. The results show that the frequency of retweets between users who are in the same community is approximately two times that between users who are in different communities. In contrast, the results also show that tweets disseminated via inter-community retweets have future popularity about 1.5-fold that of tweets disseminated via intra-community retweets. By using this fact, we construct classifiers to predict the future popularity of tweets from community-based features as well as features related to influence of users and tweet contents. Our experimental results show that contrary to our expectations, community-based features have little contributions for predicting the future popularity of tweets. This paper discusses the implications of the counterintuitive result