Many interesting real-world systems are represented as complex networks with multiple types of interactions and complicated dependency structures between layers. These interactions can be encoded as having a valence with positive links marking interactions such as trust and friendship and negative links denoting distrust or hostility. Extracting information from these negative interactions is challenging since standard topological metrics are often poor predictors of negative link formation, particularly across network layers. In this paper, we introduce a method based on mutual information which enables us to predict both negative and positive relationships. Our experiments show that SMLP (Signed Multiplex Link Prediction) can leverage negative relationship layers in multiplex networks to improve link prediction performance.
Another strategy is to create more general versions of the topological measures that capture activity patterns in multilayer networks. @cite_24 introduced a probabilistically weighted extension of the Adamic Adar measure for these networks. Weights are calculated by doing a triad census to estimate the probability of different link type combinations. The extended Adamic Adar metric is then used, along with other unsupervised link predictors, as input for a supervised classifier. Similarly, @cite_16 extend the definition of network neighborhood by considering the union of neighbors across all layers. These multilayer features are then combined in a supervised model to do link prediction. One weakness with the above mentioned models is their inability to use temporal information accrued over many snapshots, rather than relying on a single previous snapshot. In this paper, we evaluate two versions of our framework, a version that only uses topological metrics calculated from one time slice vs. multiple snapshots. @cite_9 combined multidimensional versions of Common Neighbors and Adamic Adar with predictors that are able to utilize temporal information. However, like the standard version of these metrics, these extended versions do not necessarily generalize to networks generated from different processes.
@cite_24 @cite_9 @cite_16
2007444087 1990408974 2274257646 2153378748
Missing link prediction in networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate a simple framework of link prediction on the basis of node similarity. We compare nine well-known local similarity measures on six real networks. The results indicate that the simplest measure, namely Common Neighbours, has the best overall performance, and the Adamic-Adar index performs second best. A new similarity measure, motivated by the resource allocation process taking place on networks, is proposed and shown to have higher prediction accuracy than common neighbours. It is found that many links are assigned the same scores if only the information of the nearest neighbours is used. We therefore design another new measure exploiting information on the next nearest neighbours, which can remarkably enhance the prediction accuracy. Complex networks have been receiving increasing attention by the scientific community, also due to the availability of massive network data from diverse domains. One problem largely studied so far is Link Prediction, i.e. the problem of predicting new upcoming connections in the network. However, one aspect of complex networks has been disregarded so far: real networks are often multidimensional, i.e. multiple connections may reside between any two nodes. In this context, we define the problem of Multidimensional Link Prediction, and we introduce several predictors based on structural analysis of the networks. We present the results obtained on real networks, showing the performances of both the introduced multidimensional versions of the Common Neighbors and Adamic-Adar, and the derived predictors aimed at capturing the multidimensional and temporal information extracted from the data. Our findings show that the evolution of multidimensional networks can be predicted, and that supervised models may improve the accuracy of underlying unsupervised predictors, if used in conjunction with them. Today, numerous models and metrics are available to capture and characterize static properties of online social networks. When it comes to understanding their dynamics and evolution, however, research offers little interms of metrics or models. Current metrics are limited to logical time clocks, and unable to capture interactions with external factors that rely on physical time clocks. In this paper, our goal is to take initial steps towards building a set of metrics for characterizing social network dynamics based on physical time. We focus our attention on two metrics that capture the "eagerness" of users in building social structure. More specifically, we propose metrics of link delay and triadic closure delay, two metrics that capture the time delay between when a link or triadic closure is possible,and when they actually instantiate in the trace. Considered over time or across traces, the value of these metrics can provide insight on the speed at which users act in building and extending their social neighborhoods. We apply these metrics to two real traces of social network dynamics from the Renren and Facebook networks. We show that these metrics are generally consistent across networks, but their differences reveal interesting properties of each system. We argue that they can be attributed to factors such as network maturity, environmental and social contexts, and services offered by network provider, all factors independent of the network topology and captured by our proposed metrics. Finally, we find that triadic closure delays capture the ease of neighbor discovery in social networks, and can be strongly influenced by friend recommendation systems. Feedforward multilayer networks trained by supervised learning have recently demonstrated state of the art performance on image labeling problems such as boundary prediction and scene parsing. As even very low error rates can limit practical usage of such systems, methods that perform closer to human accuracy remain desirable. In this work, we propose a new type of network with the following properties that address what we hypothesize to be limiting aspects of existing methods: (1) a wide' structure with thousands of features, (2) a large field of view, (3) recursive iterations that exploit statistical dependencies in label space, and (4) a parallelizable architecture that can be trained in a fraction of the time compared to benchmark multilayer convolutional networks. For the specific image labeling problem of boundary prediction, we also introduce a novel example weighting algorithm that improves segmentation accuracy. Experiments in the challenging domain of connectomic reconstruction of neural circuity from 3d electron microscopy data show that these "Deep And Wide Multiscale Recursive" (DAWMR) networks lead to new levels of image labeling performance. The highest performing architecture has twelve layers, interwoven supervised and unsupervised stages, and uses an input field of view of 157,464 voxels ( @math ) to make a prediction at each image location. We present an associated open source software package that enables the simple and flexible creation of DAWMR networks.