Graph clustering aims to identify clusters that feature tighter connections between internal nodes than external nodes. We noted that conventional clustering approaches based on a single vertex or edge cannot meet the requirements of clustering in a higher-order mixed structure formed by multiple nodes in a complex network. Considering the above limitation, we are aware of the fact that a clustering coefficient can measure the degree to which nodes in a graph tend to cluster, even if only a small area of the graph is given. In this study, we introduce a new cluster quality score, i.e., the local motif rate, which can effectively respond to the density of clusters in a higherorder graph. We also propose a motif-based local expansion and optimization algorithm (MLEO) to improve local higher-order graph clustering. This algorithm is a purely local algorithm and can be applied directly to higher-order graphs without conversion to a weighted graph, thus avoiding distortion of the transform. In addition, we propose a new seed-processing strategy in a higher-order graph. The experimental results show that our proposed strategy can achieve better performance than the existing approaches when using a quadrangle as the motif in the LFR network and the value of the mixing parameter µ exceeds 0.6.

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In this paper, we propose, develop and evaluate a new adaptive sampling scheme for monitoring and measuring network performance metrics in MPLS-based IP network supporting real time applications. The paper is focused on investigating the mechanisms that can adaptively a$ust the parameters of the sampling technique based on the estimated hafic rate. The pe$ormance of the proposed technique is compared with conventional sampling method by conducting simulation experiments using voice trafic pattems. Simulation results are presented to illustrate that adaptive sampling provides the potential for better monitoring, control, and management of high-performance networks with higher accuracy, lower overhead, or borh.

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