相关论文: Topic-Based Influence Computation in Social Networ…
Social networks have increasingly become important and popular in modern times. Moreover, the influence of social networks plays a vital role in various organizations including government organizations, academic research or corporate…
Influence maximization is the problem of selecting top $k$ seed nodes in a social network to maximize their influence coverage under certain influence diffusion models. In this paper, we propose a novel algorithm IRIE that integrates a new…
How social networks influence human behavior has been an interesting topic in applied research. Existing methods often utilized scale-level behavioral data to estimate the influence of a social network on human behavior. This study proposes…
Text-based personality prediction by computational models is an emerging field with the potential to significantly improve on key weaknesses of survey-based personality assessment. We investigate 3848 profiles from Twitter with self-labeled…
Influence maximization is the problem of selecting a set of influential users in the social network. Those users could adopt the product and trigger a large cascade of adoptions through the " word of mouth " effect. In this paper, we…
It is hard to detect important articles in a specific context. Information retrieval techniques based on full text search can be inaccurate to identify main topics and they are not able to provide an indication about the importance of the…
Individuals' access to information in a social network depends on its distributed and where in the network individuals position themselves. However, individuals have limited capacity to manage their social connections and process…
Community search over large graphs is a fundamental problem in graph analysis. Recent studies propose to compute top-k influential communities, where each reported community not only is a cohesive subgraph but also has a high influence…
Social networks play a fundamental role in the diffusion of innovation through peers' influence on adoption. Thus, network position including a wide range of network centrality measures have been used to describe individuals' affinity to…
Political misinformation, astroturfing and organised trolling are online malicious behaviours with significant real-world effects. Many previous approaches examining these phenomena have focused on broad campaigns rather than the small…
How can we study social interactions on evolving topics at a mass scale? Over the past decade, researchers from diverse fields such as economics, political science, and public health have often done this by querying Twitter's public API…
The last decade has seen great progress in both dynamic network modeling and topic modeling. This paper draws upon both areas to create a Bayesian method that allows topic discovery to inform the latent network model and the network…
In this paper we propose a novel control approach for opinion dynamics on evolving networks. The controls modify the strength of connections in the network, rather than influencing opinions directly, with the overall goal of steering the…
Dynamic influence maximization problem (DIMP) aims to maintain a group of influential users within an evolving social network, so that the influence scope can be maximized at any given moment. A primary category of DIMP algorithms focuses…
Many networks have event-driven dynamics (such as communication, social media and criminal networks), where the mean rate of the events occurring at a node in the network changes according to the occurrence of other events in the network.…
In many real-world applications such as social network analysis and online marketing/advertising, the community detection is a fundamental task to identify communities (subgraphs) in social networks with high structural cohesiveness. While…
Social power quantifies the ability of individuals to influence others and plays a central role in social influence networks. Yet, computing social power typically requires global knowledge and significant computational or storage…
The classic influence maximization problem finds a limited number of influential seed users in a social network such that the expected number of influenced users in the network, following an influence cascade model, is maximized. The…
We present a physics-inspired method for inferring dynamic rankings in directed temporal networks - networks in which each directed and timestamped edge reflects the outcome and timing of a pairwise interaction. The inferred ranking of each…
In online social network (OSN), understanding the factors bound to the role and strength of interaction(tie) are essential to model a wide variety of network-based applications. The recognition of these interactions can enhance the accuracy…