Dual-layer network representation exploiting information characterization

In this paper, a logical dual-layer representation approach is proposed to facilitate the analysis of directed and weighted complex networks. Unlike the single logical layer structure, which was widely used for the directed and weighted flow graph, the proposed approach replaces the single layer with a dual-layer structure, which introduces a provider layer and a requester layer. The new structure provides the characterization of the nodes by the information, which they provide to and they request from the network. Its features are explained and its implementation and visualization are also detailed. We also design two clustering methods with different strategies respectively, which provide the analysis from different points of view. The effectiveness of the proposed approach is demonstrated using a simplified example. By comparing the graph layout with the conventional directed graph, the new dual-layer representation reveals deeper insight into the complex networks and provides more opportunities for versatile clustering analysis.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)

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

Link Prediction in Social Networks Using Markov Random Field

Link prediction is an important task for social networks analysis, which also has applications in other domains such as information retrieval, recommender systems and e-commerce. The task is related to predicting the probable connection between two nodes in the netwok. These links are subjected to loss because of the improper creation or the lack of reflection of links in the networks; so it`s possible to develop or complete these networks and recycle the lost items and information through link prediction. In order to discover and predict these links we need the information of the nodes in the network. The information are usually extracted from the network`s graph and utilized as factors for recognition. There exist a variety of techniques for link prediction, amongst them, the most practical and current one is supervised learning based approach. In this approach, the link prediction is considered as binary classifier that each pair of nodes can be 0 or 1. The value of 0 indicates no connection between nodes and 1 means that there is a connection between them. In this research, while studying probabilistic graphical models, we use Markov random field (MRF) for link prediction problem in social networks. Experimentl results on Flicker dataset showed the proposed method was better than previous methods in precision and recall

Similarity Index based Link Prediction Algorithms in Social Networks: A Survey, Journal of Telecommunications and Information Technology, 2016, nr 2

Social networking sites have gained much popularity in the recent years. With millions of people connected virtually generate loads of data to be analyzed to infer meaningful associations among links. Link prediction algorithm is one such problem, wherein existing nodes, links and their attributes are analyzed to predict the possibility of potential links, which are likely to happen over a period of time. In this survey, the local structure based link prediction algorithms existing in literature with their features and also the possibility of future research directions is reported and discussed. This survey serves as a starting point for beginners interested in understanding link prediction or similarity index algorithms in general and local structure based link prediction algorithms in particular

Link prediction in graph construction for supervised and semi-supervised learning

Many real-world domains are relational in nature since they consist of a set of objects related to each other in complex ways. However, there are also flat data sets and if we want to apply graph-based algorithms, it is necessary to construct a graph from this data. This paper aims to: i) increase the exploration of graph-based algorithms and ii) proposes new techniques for graph construction from flat data. Our proposal focuses on constructing graphs using link prediction measures for predicting the existence of links between entities from an initial graph. Starting from a basic graph structure such as a minimum spanning tree, we apply a link prediction measure to add new edges in the graph. The link prediction measures considered here are based on structural similarity of the graph that improves the graph connectivity. We evaluate our proposal for graph construction in supervised and semi-supervised classification and we confirm the graphs achieve better accuracy.São Paulo Research Foundation (FAPESP) (grants: 2013/12191-5, 2011/21880-3 and 2011/22749-8