Community-Based Influence Maximization Using Network Embedding in Dynamic Heterogeneous Social Networks

Influence maximization (IM) is a very important issue in social network diffusion analysis. The topology of real social network is large-scale, dynamic, and heterogeneous. The heterogeneity, and continuous expansion and evolution of social network pose a challenge to find influential users. Existing IM algorithms usually assume that social...

Influence of clustering coefficient on network embedding in link prediction

Multiple network embedding algorithms have been proposed to perform the prediction of missing or future links in complex networks. However, we lack the understanding of how network topology affects their performance, or which algorithms are more likely to perform better given the topological properties of the network. In this paper, we...

Node Influence Prediction in Complex Networks: Towards network embedding based features

The study of epidemic spreading processes on contact based complex networks has gained a lot of traction in recent years. These processes can entail a variety of problems such as disease spreading, opinion spreading in social networks or even airport congestion in airline networks. One of the key tasks in this area of research and also of this...

Improvements To The Secure Construction And Utilization Of Greedy Embeddings In Friend-To-Friend Overlays

Routing based on greedy network embeddings enables efficient and privacypreserving routing in overlays where connectivity is restricted to mutually trusted nodes. In previous works, we proposed security enhancements to the embedding and routing procedures to protect against denial-of-service attacks by malicious overlay participants. In this...

Susceptible-infected-spreading-based network embedding in static and temporal networks

Link prediction can be used to extract missing information, identify spurious interactions as well as forecast network evolution. Network embedding is a methodology to assign coordinates to nodes in a low-dimensional vector space. By embedding nodes into vectors, the link prediction problem can be converted into a similarity comparison task....

Diffusion based temporal network embedding for link prediction

Link prediction in complex networks has attracted increasing attention. The link prediction algorithms can be used to retrieve missing information, identify spurious interactions, capturing net- work evolution, and so on. Recently, network embedding has been proposed as a new strategy to embed network into low-dimensional vector space. By...

Degree-biased random walk for large-scale network embedding

Network embedding aims at learning node representation by preserving the network topology. Previous embedding methods do not scale for large real-world networks which usually contain millions of nodes. They generally adopt a one-size-fits-all strategy to collect information, resulting in a large amount of redundancy. In this paper, we propose...