Search

The community detection problem has attracted a huge number of researchers from the scientific community because it helps to understand the relationship between structural and functional properties of social networks. In this paper, we propose a novel community detection approach namely, Closeness Similarity driven Information Diffusion based community detection (CSID) which makes full utilization of local topology for similarity computation followed by information exchange and modularity maximization. Most of the existing algorithms so far are not suitable to quantify both the local topology information and exchange of information between the nodes. Extensive experiments are carried out to perform a comparative evaluation on several real-world datasets. The experimental results show that the proposed approach gives better results in comparison to most of the baseline algorithms.

Detection in rough sea is a key issue for Doppler maritime radars intended to detect small moving targets. Indeed, heavy sea clutter has very different statistical properties from those of land clutter. A main difference is that sea clutter has an intrinsic large spectral spread. Moreover, this extent is highly inhomogeneous. These properties mean that the CFAR topologies usually used, either in non-Doppler maritime detection or in the detection of ground moving targets are not efficient in the case of coherent maritime detection (high CFAR losses on mobile targets at the border of thermal noise and clutter areas). The proposed CFAR has an adaptive structure that allows it to adapt to the local topology of the noise and sea clutter mixture.

Multipath inter-domain routing can improve the reliability, robustness and path diversity of the Internet. This paper proposes one User-customizing oriented Multi-path Inter-domain Routing protocol called as UMIR. Its basic idea is as follows: based on user routing requirements, it selects some nodes from the feasible BGP paths and requests their path-lets information, and then it builds a local topology to calculate user routes. The experimental analysis shows that this UMIR protocol can achieve rich candidate and high-quality paths.

Understanding how pathogen's proteins interact with its host's proteins is the key concept for understanding pathogen's infection mechanism, which can lead to the discovery of improved therapeutics for treating infectious diseases. Several studies suggest that proteins from various pathogens tend to interact with human proteins involved in the same biological pathway. This implies that pathogens are inclined to target host's proteins with similar function. In addition, conservation between a protein's function and its local topological structure in a protein-protein interaction network (PIN) has been previously characterized. This leads to the hypothesis that pathogens target the host's proteins with a similar local topological structure in a PIN. In this work, this hypothesis is examined by adding a graphlet degree vector of a protein in the human PIN as a feature in the prediction model and using that model to predict the protein-protein interaction between human and four pathogens. The results show that this graphlet degree vector increases the performance significantly for all pathogens. This suggests that the intraspecies protein-protein interactions should be taken into consideration when developing prediction methods for host-pathogen protein interaction. The results also support the hypothesis that there exists a relationship between a protein's function and the local topology of the PIN.

A distributed service restoration method in active distribution network with distributed generators(DG) interconnected is proposed in this paper. According to local topology, switches are divided intogroups to share local measurements. The restoration process is accomplished by the contraction of frontiers. The frontiers are the decision makers of network reconfiguration based on local communication and they will contract step by step until they meet together in a group. During contraction, the load transfer path and islanding separation will be determined. A simulation case of 10kV distribution network with DGs is studied and the whole process of service restoration is analyzed based on DIgSILENT software.

Detecting and registering nonrigid surfaces are two important research problems for computer vision. Much work has been done with the assumption that there exists only one instance in the image. In this work, we propose an algorithm that detects and registers multiple nonrigid instances of given objects in a cluttered image. Specifically, after we use low level feature points to obtain the initial matches between templates and the input image, a novel high-order affinity graph is constructed to model the consistency of local topology. A hierarchical clustering approach is then used to locate the nonrigid surfaces. To remove the outliers in the cluster, we propose a deterministic annealing approach based on the Thin Plate Spline (TPS) model. The proposed method achieves high accuracy even when the number of outliers is nineteen times larger than the inliers. As the matches may appear sparsely in each instance, we propose a TPS based match growing approach to propagate the matches. Finally, an approach that fuses feature and appearance information is proposed to register each nonrigid surface. Extensive experiments and evaluations demonstrate that the proposed algorithm achieves promising results in detecting and registering multiple non-rigid surfaces in a cluttered scene.

We present a new approach to the quantifying changes in an email social network illustrated by the data from the Enron dataset). The Triad Transition Matrix containing the probabilities of transitions between triads is defined, then we show how it can help to discover the dynamic patterns of network evolution. The compatibility of the TTM approach with the existing methods of characterizing the local topology (network motifs) is discussed as well.

International audience; Mobility models allow to mimic the behavior of mobile nodes when MANET performances are evaluated via simulations. Nevertheless, simulations results are strongly correlated with the mobility model. In this work, we introduce new metrics in order to characterize any mobility model. These metrics reflect the nodes distribution, the local topology changes, the repetitive behavior of a model, etc. They allow in consequence to characterize mobility models, and particularly to introduce a taxonomy based on objective and quantitative criteria. Using these metrics, we characterize some individual and one group mobility models.

Many peer-to-peer anonymity protocols have been proposed in recent years. One of the key challenges in designing such protocols is peer discovery, especially in decentralized unstructured peer-to-peer systems. In this paper, we propose a gossip algorithm for peer discovery based upon node local in-degree (GPDL), using only limited knowledge of the network topology. Our goal is try to discover peers uniformly in the whole network as well as try to discover robust nodes based upon local topology information. The idea behind our gossip is that the larger the degree of a node, the better its robustness. Our simulations show that the network under GPDL algorithm is high clustering, and its average diameter is almost not influenced by the node dynamics. With time passing, a node can discover robust nodes, and its neighbors are nearly selected uniformly from the whole network. The message overhead produced by GPDL is small

High-throughput generation of new types of relational biological datasets is creating a demand for network-based signal processing and pattern recognition to provide new insights. Such networks are often too large to interpret visually and too complicated to be explained solely based on local topological properties. Just as signal processing and statistical techniques have been used in traditional, sequential-based biological datasets, so too are methodologies needed to automatically discern patterns in the huge, emerging networks. One way to do this is by transforming these very large networks into discernable epitomes, or abstracts, of the original networks. This work presents an approach for doing this via topological compression. Through capturing nodes' global topologies and subsequent compression, a new network epitome can be derived. Here, this is done with an E. Coli gene regulation network, resulting in biological findings that could not be derived from the local topology of the original network.

The quality of the clock distribution networks (CDNs) impacts the performance of an SoC. In this paper, we propose local topology modification (LTM) in order to enhance the CDN's quality in terms of total wire length and wire elongations. The incorporation of this method into a deferred-merge embedding algorithm (DME) and greedy-DME (GDME) reduces the total wire length by around 7.83% and 9.77% respectively, with a slight increase in run time. In addition, we show that GDME relies intensively on wire elongations and it offers a solution that suffers from high standard deviation of the path lengths between clock pins and the CDN's root (SDPL). Applying LTM to GDME reduces wire elongations and SDPL by 96.4 % and 51.5 % respectively.