Multi-agent networks are often modeled as interaction graphs, where the nodes represent the agents and the edges denote some direct interactions. The robustness of a multi-agent network to perturbations such as failures, noise, or malicious attacks largely depends on the corresponding graph. In many applications, networks are desired to have well-connected interaction graphs with relatively small number of links. One family of such graphs is the random regular graphs. In this paper, we present a decentralized scheme for transforming any connected interaction graph with a possibly non-integer average degree of @math into a connected random @math -regular graph for some @math . Accordingly, the agents improve the robustness of the network while maintaining a similar number of links as the initial configuration by locally adding or removing some edges.
In the literature, there are many works related to the construction of robust interaction graphs. Some of these works consider how a robustness measure can be improved via some modifications to the graph topology. For instance, such improvement can be achieved by rewiring a small percentage of the existing edges (e.g., @cite_7 ) or adding a small number of edges to the graph (e.g., @cite_44 @cite_15 ).
@cite_44 @cite_15 @cite_7
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An important property of networked systems is their robustness against removal of network nodes, through either random node failure or targeted attack. Although design methods have been proposed for creating, ab initio, a network that has optimal robustness according to a given measure, one is often instead faced with an existing network that cannot feasibly be substantially modified or redesigned, yet whose robustness can be improved by a lesser degree of modification. We present empirical results that show how robustness, as measured either by the size of the largest connected component or by the shortest path length between pairs of nodes, is affected by several different strategies that alter the network by rewiring a fraction of the edges or by adding new edges. We find that a modest alteration of an initially scale-free network can usefully improve robustness against attack, particularly when the fraction of attacked nodes is small, and we identify modification schemes that are most effective for this purpose. Abstract Robustness of networked systems against noise corruption and structural changes in an underlying network topology is a critical issue for a reliable performance. In this paper, we investigate this issue of robustness in networked systems both from structural and functional viewpoints. Structural robustness deals with the effect of changes in a graph structure due to link or edge failures, while functional robustness addresses how well a system behaves in the presence of noise. We discuss that both of these aspects are inter-related, and can be measured through a common graph invariant. A graph process is introduced where edges are added to an existing graph in a step-wise manner to maximize robustness. Moreover, a relationship between the symmetry of an underlying network structure and robustness is also discussed. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results.