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摘要翻译：
本文提出了一种新的随机流言算法--贪婪窃听流言算法(GGE)，用于平均共识问题的分布式计算。在gossip算法中，网络中的节点随机地与邻居通信，迭代地交换信息。该算法简单、分散，对无线网络应用具有吸引力。一般说来，gossip算法对不可靠的无线环境和时变的网络拓扑具有很强的鲁棒性。在本文中，我们引入了GGE，并证明了贪婪更新导致快速收敛。我们不要求节点具有任何位置信息。相反，贪婪的更新是通过利用无线通信的广播性质来实现的。在GGE的运行过程中，当一个节点决定闲聊时，它不是随机选择一个邻居，而是贪婪选择，选择与自己值相差最大的节点。为了进行这种选择，节点需要知道其邻居的值。因此，我们假设所有传输都是无线广播，节点通过窃听邻居的通信来跟踪邻居的值。我们证明了对于连通网络拓扑，GGE的收敛性是保证的。我们还研究了GGE的收敛速度，并通过理论界和数值模拟说明了GGE在中等大小的随机几何图拓扑上的收敛速度一直优于随机gossip,并与地理gossip相比较。
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英文标题：
《Greedy Gossip with Eavesdropping》
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作者：
Deniz Ustebay, Boris Oreshkin, Mark Coates, Michael Rabbat
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Distributed, Parallel, and Cluster Computing        分布式、并行和集群计算
分类描述：Covers fault-tolerance, distributed algorithms, stabilility, parallel computation, and cluster computing. Roughly includes material in ACM Subject Classes C.1.2, C.1.4, C.2.4, D.1.3, D.4.5, D.4.7, E.1.
包括容错、分布式算法、稳定性、并行计算和集群计算。大致包括ACM学科类C.1.2、C.1.4、C.2.4、D.1.3、D.4.5、D.4.7、E.1中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  This paper presents greedy gossip with eavesdropping (GGE), a novel randomized gossip algorithm for distributed computation of the average consensus problem. In gossip algorithms, nodes in the network randomly communicate with their neighbors and exchange information iteratively. The algorithms are simple and decentralized, making them attractive for wireless network applications. In general, gossip algorithms are robust to unreliable wireless conditions and time varying network topologies. In this paper we introduce GGE and demonstrate that greedy updates lead to rapid convergence. We do not require nodes to have any location information. Instead, greedy updates are made possible by exploiting the broadcast nature of wireless communications. During the operation of GGE, when a node decides to gossip, instead of choosing one of its neighbors at random, it makes a greedy selection, choosing the node which has the value most different from its own. In order to make this selection, nodes need to know their neighbors' values. Therefore, we assume that all transmissions are wireless broadcasts and nodes keep track of their neighbors' values by eavesdropping on their communications. We show that the convergence of GGE is guaranteed for connected network topologies. We also study the rates of convergence and illustrate, through theoretical bounds and numerical simulations, that GGE consistently outperforms randomized gossip and performs comparably to geographic gossip on moderate-sized random geometric graph topologies.
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PDF链接：
https://arxiv.org/pdf/0909.1830

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