网络考古学：从当今的互动中揭示古代网络
Network Archaeology: Uncovering Ancient Networks from Present-day Interactions
摘要：
经常会出现关于旧的或灭绝的网络的问题。什么样的蛋白质在早已灭绝的酵母祖先物种中相互作用？3年前，谁是Last.fm社交网络的核心玩家？我们回答这些问题的能力受到过去网络版本不可用的限制。为了克服这些局限性，我们提出了几种算法来重建网络的增长历史，只考虑到网络，因为它存在的今天和一个生成模型，网络被认为已经演变。我们的基于似然的方法通过反转前向增长模型来找到网络的可能先前状态。这种方法保留了节点身份，以便可以跟踪各个节点的历史记录。我们应用这些算法来揭示旧的，不存在的生物和社交网络相信已经通过几个模型，包括互补性的复制突变，森林火灾和优先连接增长。通过对合成数据和真实数据的实验，我们发现我们的算法可以估计节点到达时间，识别新节点复制链接的锚节点，并可以揭示早已消失的网络的重要特征。
英文摘要：
Often questions arise about old or extinct networks. What proteins interacted in a long-extinct ancestor species of yeast? Who were the central players in the Last.fm social network 3 years ago? Our ability to answer such questions has been limited by the unavailability of past versions of networks. To overcome these limitations, we propose several algorithms for reconstructing a network's history of growth given only the network as it exists today and a generative model by which the network is believed to have evolved. Our likelihood-based method finds a probable previous state of the network by reversing the forward growth model. This approach retains node identities so that the history of individual nodes can be tracked. We apply these algorithms to uncover older, non-extant biological and social networks believed to have grown via several models, including duplication-mutation with complementarity, forest fire, and preferential attachment. Through experiments on both synthetic and real-world data, we find that our algorithms can estimate node arrival times, identify anchor nodes from which new nodes copy links, and can reveal significant features of networks that have long since disappeared.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
