The paper presents a modification of the self-organizing Kohonen networks for more efficient coping with complex, multidimensional cluster-analysis problems. The essence of modification consists in allowing the neuron chain as the learning progresses to disconnect and later to reconnect again. First, the operation of the modified approach has been illustrated by means of synthetic data set. The...

This paper studied the cluster synchronization of directed complex networks with time delays. It is different from undirected networks, the coupling configuration matrix of directed networks cannot be assumed as symmetric or irreducible. In order to achieve cluster synchronization, this paper uses an adaptive controller on each node and an adaptive feedback strategy on the nodes which in-degree...

Clavicipitaceous fungal endophytes of the genera Epichloë and Neotyphodium form symbioses with grasses of the subfamily Pooideae, in which they can synthesize an array of bioprotective alkaloids. Some strains produce the ergopeptine alkaloid ergovaline, which is implicated in livestock toxicoses caused by ingestion of endophyte-infected grasses. Cloning and analysis of a nonribosomal peptide sy...

c © 2007 by John von Neumann Institute for Computing Permission to make digital or hard copies of portions of this work for personal or classroom use is granted provided that the copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise requires prior specific permission by the publisher ment...

This paper proposes a novel channel estimation method and a cluster-based opportunistic scheduling policy, for a wireless energy transfer (WET) system consisting of multiple low-complex energy receivers (ERs) with limited processing capabilities. Firstly, in the training stage, the energy transmitter (ET) obtains a set of Received Signal Strength Indicator (RSSI) feedback values from all ERs, a...

Abstract. Community detection is of great importance for understanding graph structure in social networks. The communities in real-world networks are often overlapped, i.e. some nodes may be a member of multiple clusters. How to uncover the overlapping communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, a novel algorithm to identify...

Network models with preferential attachment, where new nodes are injected into the network and form links with existing nodes proportional to their current connectivity, have been well studied for some time. Extensions have been introduced where nodes attach proportionally to arbitrary fitness functions. However, in these models, attaching to a node always increases the ability of that node to ...

The degree distribution is an important characteristic of complex networks. In many data analysis applications, the networks should be represented as fixed-length feature vectors and therefore the feature extraction from the degree distribution is a necessary step. Moreover, many applications need a similarity function for comparison of complex networks based on their degree distributions. Such...

Identification and classification of overlap nodes in communities is an important topic in data mining. In this paper, a new clustering method to uncover overlap nodes in complex networks is proposed. It is based on particles walking and competing with each other, using randomdeterministic movement. The new community detection algorithm can output not only hard labels, but also continuous-value...

A major problem in the study of complex socioeconomic systems is represented by privacy issues— that can put severe limitations on the amount of accessible information, forcing to build models on the basis of incomplete knowledge. In this paper we investigate a novel method to reconstruct global topological properties of a complex network starting from limited information. This method uses the ...