Inter-cluster Links Research Articles

Color-encoded Links Improve Homophily Perception in Node-Link Diagrams.

Node-link diagrams enable visual assessment of homophily when viewers can identify and evaluate the relative number of intra-cluster and inter-cluster links. Our online experiment shows that a new design with link type encoded edge color leads to more accurate perception of homophily than a design with same-color edges.

Multi-Tasking Memcapacitive Networks

Recent studies have shown that networks of memcapacitive devices provide an ideal computing platform of low power consumption for reservoir computing systems. Random, crossbar, or small-world power-law (SWPL) structures are common topologies for reservoir substrates to compute single tasks. However, neurological studies have shown that the interconnections of cortical brain regions associated with different functions form a rich-club structure. This structure allows human brains to perform multiple activities simultaneously. So far, memcapacitive reservoirs can perform only single tasks. Here, we propose, for the first time, cluster networks functioning as memcapacitive reservoirs to perform multiple tasks simultaneously. Our results illustrate that cluster networks surpassed crossbar and SWPL networks by factors of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.1\times, 5.2\times $ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.7\times $ </tex-math></inline-formula> on three tasks: Isolated Spoken Digits, MNIST, and CIFAR-10. Compared to single-task networks in our previous and published results, multitasking cluster networks could accomplish similar accuracies of 86%, 94.4%, and 27.9% for MNIST, Isolated Spoken Digits, and CIFAR-10. Our extended simulations reveal that both the input signal amplitudes and the inter-cluster connections contribute to the accuracy of cluster networks. Selecting optimal values for signal amplitudes and inter-cluster links is key to obtaining high classification accuracy and low power consumption. Our results illustrate the promise of memcapacitive brain-inspired cluster networks and their capability to solve multiple tasks simultaneously. Such novel computing architectures have the potential to make edge applications more efficient and allow systems that cannot be reconfigured to solve multiple tasks.

Improving the strength of polyethylene solids by simple controlling of the molecular weight distribution

We prepared model polyethylene (PE) samples that contain the controlled-amounts of the high-molecular-weight component by blending a monodispersed PE into a polydispersed PE, to elucidate the influence of the high-molecular-weight component of a polymer on its mechanical properties. The strength and strain-hardening modulus were significantly enhanced by the addition of a monodispersed PE of sufficiently high molecular weight, and the critical value of Mw was determined to be ~3.0 × 105. The improved strength and strain-hardening modulus are attributed to higher amounts of inter-cluster links that bridge adjacent lamellar cluster units because these links act as stress transmitters between these units.

Similarity preserving overlapping community detection in signed networks

Community detection in signed networks is a challenging research problem, and is of great importance to understanding the structural and functional properties of signed networks. It aims at dividing nodes into different clusters with more intra-cluster and less inter-cluster links. Meanwhile, most positive links should lie within clusters and most negative links should lie between clusters. In recent years, some methods for community detection in signed networks have been proposed, but few of them focus on overlapping community detection. Moreover, most of them directly exploit the sparse link topology to detect communities, which often makes them perform poorly. In view of this, in this paper we propose a similarity preserving overlapping community detection (SPOCD) method. SPOCD firstly extracts node similarity information and geometric structure information from the link topology, and then uses a graph regularized binary semi-nonnegative matrix factorization (GRBSNMF) model to fuse these two sources of information to detect communities. Through this mechanism, nodes with high similarity can be well preserved in the same community. Besides, SPOCD devises a special discretization strategy to obtain the binary community indicator matrix, which is very convenient for directly identifying overlapping communities in signed networks. We conduct extensive experiments on synthetic and real-world signed networks, and the results demonstrate that our method outperforms state-of-the-art methods.

Design and Improvement of Routing Protocol for Field Observation Instrument Networking Based on LEACH Protocol

Field observation instruments in cold and arid areas are deployed with many difficulties caused by the harsh natural environment, which leads to the lag of information acquisition ability and severely restricts the geoscience research in these areas. Therefore, it is urgent to study the suitable routing technology of observation instrument networks according to the characteristics of cold and arid areas. In this paper, we have studied and designed an improved routing protocol for the field observation instruments network based on the LEACH protocol (FOI-LEACH). Firstly, the FOI-LEACH was proposed to mainly improve the LEACH protocol in three aspects: (1) the network nodes are heterogeneous and combined with the characteristics of field observation instrument networking. The residual energy and the rechargeable energy of nodes are added in the process of cluster head (CH) election to reduce the risk of premature death of CHs and shortened network life cycle caused by the selection of nodes with less energy as CH. (2) In the process of cluster forming, the distance from CH to the base station (BS) and the residual energy of CH is considered when setting the cluster radius, to reasonably plan the cluster size and alleviate the “hot spot” problem. The nonuniform distribution of clusters in the network is enhanced to balance the total network energy consumption. (3) The autonomous zone-based multihop routing mechanism is adopted to solve the low reliability of data transmission caused by the poor quality of intercluster communication links and premature death of nodes in long-distance transmission. Then, MATLAB was used to compare the network routing protocol model of the observation instrument from four aspects, network life, energy consumption rate, stability, and throughput. The results showed that the improved algorithm FOI-LEACH balances the network energy consumption and alleviates the “hot spot” problem, to extend the lifetime of network nodes.

Improving the reliability of Byzantine fault‐tolerant distributed software‐defined networks

SummaryThis paper presents a method to improve the reliability and fault tolerance of distributed software‐defined networks. This method is called “BIRDSDN (Byzantine‐Resilient Improved Reliable Distributed Software‐Defined Networks).” In BIRDSDN, a group communication is implemented among the controllers of the whole clusters. This method can detect the crash failure and Byzantine failure of any controller and undertakes a fast detection and recovery scheme to select the controllers to take over the orphan switches. BIRDSDN takes into account the reliability of the nodes considering the failure probability of intracluster and intercluster links, topology, load, and latency. The numerical results show that this approach performs better than the other approaches regarding failure detection, recovery, latency, throughput, reliability, and packet loss.

Secure Optical Communication Based on Cluster Chaos Synchronization in Semiconductor Lasers Network

We propose and numerically demonstrate a secure optical communication scheme in a small-world semiconductor lasers network based on cluster chaos synchronization and false-message scrambling. According to the symmetries of network, the semiconductor lasers are divided into sets of heterogeneous coupled clusters. The properties of cluster chaos synchronization, the performance and security of optical chaotic communication, as well as the influence of false message on the synchronization of clusters are systematically investigated. It is demonstrated that, by properly setting the parameters of the lasers network, high-quality chaos synchronization can be achieved between the intra-cluster lasers, whereas incoherent states are observed between inter-cluster lasers. Moreover, by inserting false messages into the inter-cluster couplings, the privacy of chaotic carriers transmitted over the inter-cluster public links can be significantly enhanced, as such secure bidirectional communication among intra-cluster lasers is achievable with a message bit rate of several Gbit/s. Compared with the conventional point-to-point communication systems, the proposed scheme supports multipoint-to-multipoint secure communications.

Scaling relations and finite-size scaling in gravitationally correlated lattice percolation models

In some systems, the connecting probability (and thus the percolation process) between two sites depends on the geometric distance between them. To understand such process, we propose gravitationally correlated percolation models for link-adding networks on the two-dimensional lattice G with two strategies Smax and Smin, to add a link li,j to connect site i and site j with mass mi and mj, respectively; mi and mj are sizes of the clusters which contain site i and site j, respectively. The probability to add the link li,j is related to the generalized gravity gij≡mimj/rijd, where rij is the geometric distance between i and j, and d is an adjustable decaying exponent. In the beginning of the simulation, all sites of G are occupied and there is no link. In the simulation process, two inter-cluster links li,j and lk,n are randomly chosen and the generalized gravities gij and gkn are computed. In the strategy Smax, the link with larger generalized gravity is added. In the strategy Smin, the link with smaller generalized gravity is added, which include percolation on the Erdös-Rényi random graph and the Achlioptas process of explosive percolation as the limiting cases, d → ∞ and d → 0, respectively. Adjustable strategies facilitate or inhibit the network percolation in a generic view. We calculate percolation thresholds Tc and critical exponents β by numerical simulations. We also obtain various finite-size scaling functions for the node fractions in percolating clusters or arrival of saturation length with different intervening strategies.

An efficient data collection and load balance algorithm in wireless sensor networks

The nature of multi-hop data transmission in wireless sensor network will cause serious load unbalance which will produce great restrains in related applications considering the limited energy resource. Relative load balance algorithms are usually performed inside the clusters without considering about the energy consumption of the whole network. A cluster-based balanced energy consumption algorithm (BECA) is proposed by introducing in multiple inter-cluster links to distribute the load, so as to achieve global load balance. Moreover, an efficient data collecting mechanism is proposed based on BECA to improve the traffic balance further. Simulating results based on NS2 show that BECA can obtain better balance properties and prolong the network lifetime effectively.

A decomposition-based multi-objective optimization for simultaneous balance computation and transformation in signed networks

Many social systems have a set of opposite interactions such as friend/enemy, cooperation/competition and support/opposition. In these signed systems, there exist functional imbalances from the system-level view because of the existence of unbalanced interactions. However, it is difficult to compute the unbalance degree and transform unbalanced factors to balanced ones in real signed systems. Earlier studies tackled these two issues separately and gave a unique solution, and thus cannot be well applied to real applications with constraints. In this paper, we devise a decomposition-based and network-specific multi-objective optimization algorithm to solve the balance computation and transformation of signed networks simultaneously. The devised algorithm aims at finding a set of optimal balance transformation solutions, and each of which is the trade-off between the twin objectives (i.e., the minimization of inter-cluster positive links and the minimization of intra-cluster negative links). Of these solutions, the one with the fewest unbalanced links corresponds to the solution to the balance computation. And each trade-off solution corresponds to an optimal balance transformation way under a certain transformation cost. Extensive experiments on four social networks demonstrate the effectiveness of the devised algorithm on both the computation and the transformation of structural balance. They also show that the devised algorithm can provide multiple optimal solutions at the same transformation cost.

Observer-based cluster consensus control of high-order multi-agent systems

The cluster consensus problem of high-order multi-agent systems is considered in this note with an observer-based control scheme. Sufficient condition for cluster consensus is presented in terms of easily checkable algebraic topology criterion. It is found that intra-cluster balanced topologies with antisymmetric (i.e., bidirectional and opposite signed) inter-cluster links promote cluster consensus, irrelevant of the magnitudes of intra-cluster coupling weights. The effectiveness of the theoretical results is illustrated by a numerical example.

Ultrathin percolated WO3 cluster film and its resistive response to H2

Thin films composed of tungsten oxide (WO3) nanoclusters were fabricated by oxidizing supersonic cluster beam deposited tungsten films at various temperatures. Oxidation at 700°C resulted in aggregation of the deposits, forming a percolated network of WO3 spherical caps connected by fine links. The resistance response of the palladium-(Pd-) coated film sample to hydrogen (H2) was investigated. The response rate was faster than those of other samples oxidized at lower temperatures. This is the result of the rapid electrical switching of the intercluster links between the highly resistive depleted state and conducting hydrogenated state. The possibility of improving the H2 sensing response rate with the use of the percolated WO3 film structure is illustrated.

Cluster-Based Collaborative Filtering for Sign Prediction in Social Networks with Positive and Negative Links

Social network analysis and mining get ever-increasingly important in recent years, which is mainly due to the availability of large datasets and advances in computing systems. A class of social networks is those with positive and negative links. In such networks, a positive link indicates friendship (or trust), whereas links with a negative sign correspond to enmity (or distrust). Predicting the sign of the links in these networks is an important issue and has many applications, such as friendship recommendation and identifying malicious nodes in the network. In this manuscript, we proposed a new method for sign prediction in networks with positive and negative links. Our algorithm is based first on clustering the network into a number of clusters and then applying a collaborative filtering algorithm. The clusters are such that the number of intra-cluster negative links and inter-cluster positive links are minimal, that is, the clusters are socially balanced as much as possible (a signed graph is socially balanced if it can be divided into clusters with all positive links inside the clusters and all negative links between them). We then used similarity between the clusters (based on the links between them) in a collaborative filtering algorithm. Our experiments on a number of real datasets showed that the proposed method outperformed previous methods, including those based on social balance and status theories and one based on a machine learning framework (logistic regression in this work).

Measuring relatedness between communities in a citation network

As academic disciplines are segmented and specialized, it becomes more difficult to capture relevant research areas precisely by common retrieval strategies using either keywords or journal categories. This paper proposes a method of measuring the relatedness among sets of academic papers in order to detect unrelated communities which are not related to target topic. A citation network, extracted by given keywords, is divided into communities based on the density of links. We measured and compared four measures of relatedness between two communities in a citation network for three large-scale citation datasets. We used both link and semantic similarities. The topological distance from the center in a citation network is a more efficient measure for removing the unrelated communities than the other three measures: the ratio of the number of intercluster links over the all links, the ratio of the number of common terms over all terms, cosine similarity of tf-idf vectors.

Mining near-duplicate graph for cluster-based reranking of web video search results

Recently, video search reranking has been an effective mechanism to improve the initial text-based ranking list by incorporating visual consistency among the result videos. While existing methods attempt to rerank all the individual result videos, they suffer from several drawbacks. In this article, we propose a new video reranking paradigm called cluster-based video reranking (CVR). The idea is to first construct a video near-duplicate graph representing the visual similarity relationship among videos, followed by identifying the near-duplicate clusters from the video near-duplicate graph, then ranking the obtained near-duplicate clusters based on cluster properties and intercluster links, and finally for each ranked cluster, a representative video is selected and returned. Compared to existing methods, the new CVR ranks clusters and exhibits several advantages, including superior reranking by utilizing more reliable cluster properties, fast reranking on a small number of clusters, diverse and representative results. Particularly, we formulate the near-duplicate cluster identification as a novel maximally cohesive subgraph mining problem. By leveraging the designed cluster scoring properties indicating the cluster's importance and quality, random walk is applied over the near-duplicate cluster graph to rank clusters. An extensive evaluation study proves the novelty and superiority of our proposals over existing methods.

Cluster synchronization in networks of distinct groups of maps

In this paper, we study cluster synchronization in general bi-directed networks of nonidentical clusters, where all nodes in the same cluster share an identical map. Based on the transverse stability analysis, we present sufficient conditions for local cluster synchronization of networks. The conditions are composed of two factors: the common inter-cluster coupling, which ensures the existence of an invariant cluster synchronization manifold, and communication between each pair of nodes in the same cluster, which is necessary for chaos synchronization. Consequently, we propose a quantity to measure the cluster synchronizability for a network with respect to the given clusters via a function of the eigenvalues of the Laplacian corresponding to the generalized eigenspace transverse to the cluster synchronization manifold. Then, we discuss the clustering synchronous dynamics and cluster synchronizability for four artificial network models: (i) p-nearest-neighborhood graph; (ii) random clustering graph; (iii) bipartite random graph; (iv) degree-preferred growing clustering network. From these network models, we are to reveal how the intra-cluster and inter-cluster links affect the cluster synchronizability. By numerical examples, we find that for the first model, the cluster synchronizability regularly enhances with the increase of p, yet for the other three models, when the ratio of intra-cluster links and the inter-cluster links reaches certain quantity, the clustering synchronizability reaches maximal.

Job co-allocation strategies for multiple high performance computing clusters

To more effectively use a network of high performance computing clusters, allocating multi-process jobs across multiple connected clusters becomes an attractive possibility. This allocation process entails dividing the processes of a job among several clusters, which we refer to as co-allocation. Co-allocation offers the possibility of more efficient use of computer resources, reduced turn-around time and computations using numbers of processes larger than processes on any single cluster. In order to realize these possibilities, effective co-allocation, ultimately, depends on the inter-cluster communication cost. In this paper, we introduce a scalable co-allocation strategy called the Maximum Bandwidth Adjacent cluster Set (MBAS) strategy. The strategy makes use of two thresholds to control allocation: one to control the limit on bandwidth on usable inter-cluster communication links and another to control how jobs are split. A simulator that can simulate the dynamic behavior of jobs running across multiple clusters was developed and used to examine the performance of the MBAS co-allocation strategy. Our results indicate that by adjusting the thresholds for link level control and chunk size control in splitting jobs, the MBAS co-allocation strategy can significantly improve both user satisfaction and system utilization.

Transition to global synchronization in clustered networks

A clustered network is characterized by a number of distinct sparsely linked subnetworks (clusters), each with dense internal connections. Such networks are relevant to biological, social, and certain technological networked systems. For a clustered network the occurrence of global synchronization, in which nodes from different clusters are synchronized, is of interest. We consider Kuramoto-type dynamics and obtain an analytic formula relating the critical coupling strength required for global synchronization to the probabilities of intracluster and intercluster connections, and provide numerical verification. Our work also provides direct support for a previous spectral-analysis-based result concerning the role of random intercluster links in enhancing the synchronizability of a clustered network.

Synchronization in complex clustered networks

Synchronization in complex networks has been an active area of research in recent years. While much effort has been devoted to networks with the small-world and scale-free topology, structurally they are often assumed to have a single, densely connected component. Recently it has also become apparent that many networks in social, biological, and technological systems are clustered, as characterized by a number (or a hierarchy) of sparsely linked clusters, each with dense and complex internal connections. Synchronization is fundamental to the dynamics and functions of complex clustered networks, but this problem has just begun to be addressed. This paper reviews some progress in this direction by focusing on the interplay between the clustered topology and network synchronizability. In particular, there are two parameters characterizing a clustered network: the intra-cluster and the inter-cluster link density. Our goal is to clarify the roles of these parameters in shaping network synchronizability. By using theoretical analysis and direct numerical simulations of oscillator networks, it is demonstrated that clustered networks with random inter-cluster links are more synchronizable, and synchronization can be optimized when inter-cluster and intra-cluster links match. The latter result has one counterintuitive implication: more links, if placed improperly, can actually lead to destruction of synchronization, even though such links tend to decrease the average network distance. It is hoped that this review will help attract attention to the fundamental problem of clustered structures/synchronization in network science.

Abnormal Synchronization in Complex Clustered Networks

Recent research has revealed that complex networks with a smaller average distance and more homogeneous degree distribution are more synchronizable. We find, however, that synchronization in complex, clustered networks tends to obey a different set of rules. In particular, the synchronizability of such a network is determined by the interplay between intercluster and intracluster links. The network is most synchronizable when the numbers of the two types of links are approximately equal. In the presence of a mismatch, increasing the number of intracluster links, while making the network distance smaller, can counterintuitively suppress or even destroy the synchronization. We provide theory and numerical evidence to establish this phenomenon.