In this paper, we address the challenge of discovering hidden nodes in unknown social networks, formulating three types of hidden-node discovery problems, namely, Sybil-node discovery, peripheral-node discovery, and influencer discovery. We tackle these problems by employing a graph exploration framework grounded in machine learning. Leveraging the structure of the subgraph gradually obtained from graph exploration, we construct prediction models to identify target hidden nodes in unknown social graphs. Through empirical investigations of real social graphs, we investigate the efficiency of graph exploration strategies in uncovering hidden nodes. Our results show that our graph exploration strategies discover hidden nodes with an efficiency comparable to that when the graph structure is known. Specifically, the query cost of discovering 10% of the hidden nodes is at most only 1.2 times that when the topology is known, and the query-cost multiplier for discovering 90% of the hidden nodes is at most only 1.4. Furthermore, our results suggest that using node embeddings, which are low-dimensional vector representations of nodes, for hidden-node discovery is a double-edged sword: it is effective in certain scenarios but sometimes degrades the efficiency of node discovery. Guided by this observation, we examine the effectiveness of using a bandit algorithm to combine the prediction models that use node embeddings with those that do not, and our analysis shows that the bandit-based graph exploration strategy achieves efficient node discovery across a wide array of settings.

Identifying influencers in a given social network has become an important research problem for various applications, including accelerating the spread of information in viral marketing and preventing the spread of fake news and rumors. The literature contains a rich body of studies on identifying influential source spreaders who can spread their own messages to many other nodes. In contrast, the identification of influential brokers who can spread other nodes' messages to many nodes has not been fully explored. Theoretical and empirical studies suggest that involvement of both influential source spreaders and brokers is a key to facilitating large-scale information diffusion cascades. Therefore, this paper explores ways to identify influential brokers from a given social network. By using three social media datasets, we investigate the characteristics of influential brokers by comparing them with influential source spreaders and central nodes obtained from centrality measures. Our results show that (i) most of the influential source spreaders are not influential brokers (and vice versa) and (ii) the overlap between central nodes and influential brokers is small (less than 15%) in Twitter datasets. We also tackle the problem of identifying influential brokers from centrality measures and node embeddings, and we examine the effectiveness of social network features in the broker identification task. Our results show that (iii) although a single centrality measure cannot characterize influential brokers well, prediction models using node embedding features achieve F1 scores of 0.35--0.68, suggesting the effectiveness of social network features for identifying influential brokers.

Although beneficial information abounds on social media, the dissemination of harmful information such as so-called "fake news" has become a serious issue. Therefore, many researchers have devoted considerable effort to limiting the diffusion of harmful information. A promising approach to limiting diffusion of such information is link deletion methods in social networks. Link deletion methods have been shown to be effective in reducing the size of information diffusion cascades generated by synthetic models on a given social network. In this study, we evaluate the effectiveness of link deletion methods on Twitter by using actual logs of retweet cascades, rather than by using synthetic diffusion models. Our results show that even after deleting 50% of links detected by the NetMelt method from a Twitter social network, the size of tweet cascades after link deletion is estimated to be only 50% the original size, which suggests that the effectiveness of the link deletion strategy for suppressing information diffusion on Twitter is limited. Moreover, our results also show that there is a considerable number of cascades with many seed users, which renders link deletion methods inefficient.

While online communities are important platforms for various social activities, many online communities fail to survive, which motivates researchers to investigate factors affecting the growth and survival of online communities. We comprehensively examine the effects of a wide variety of social network features on the growth and survival of communities in Reddit. We show that several social network features, including clique ratio, density, clustering coefficient, reciprocity and centralization, have significant effects on the survival of communities. In contrast, we also show that social network features examined in this paper only have weak effects on the growth of communities. Moreover, we conducted experiments predicting future growth and survival of online communities from social network features. The results show that social network features are useful for predicting the survival of communities but not for predicting their growth.

Influence maximization is a combinatorial optimization problem on a graph: Given a social network, an influence maximization algorithm aims to find a set of influential (seed) nodes in the network such that the expected number of nodes influenced by the seed nodes is maximized under the given cascade model. Most influence maximization algorithms proposed in the literature assume that ground-truth influence spread probabilities are available. In reality, however, it is natural to assume that there exists a deviation of the influence spread probability used in the influence maximization algorithms from actual influence spread probability. In this paper, we examine the robustness of existing influence maximization algorithms against non-adversarial perturbations in influence spread probabilities. Our results show that the effectiveness of state-of-the-art approximation and heuristic algorithms may be significantly degraded, and lightweight heuristic algorithms can outperform state-of-the-art algorithms when the perturbations are large.

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.

We propose a novel problem called influence maximization for unknown graphs, and propose a heuristic algorithm for the problem. Influence maximization is the problem of detecting a set of influential nodes in a social network, which represents social relationships among individuals. Influence maximization has been actively studied, and several algorithms have been proposed in the literature. The existing algorithms use the entire topological structure of a social network. In practice, however, complete knowledge of the topological structure of a social network is typically difficult to obtain. We therefore tackle an influence maximization problem for unknown graphs. As a solution for this problem, we propose a heuristic algorithm, which we call IMUG (Influence Maximization for Unknown Graphs). Through extensive simulations, we show that the proposed algorithm achieves 60--90% of the influence spread of the algorithms using the entire social network topology, even when only 1--10% of the social network topology is known. These results indicate that we can achieve a reasonable influence spread even when knowledge of the social network topology is severely limited.