标题： 推断信息级联的潜在结构 显示英文标题

标题： Inferring the Underlying Structure of Information Cascades

摘要： 在社交网络中，信息和影响力以级联的方式在用户之间扩散。 虽然研究级联的重要性已在各种应用中得到认可，但在实践中很难观察到级联的完整结构。 此外，关于如何基于部分观测数据推断级联了解甚少。 在本文中，我们研究了遵循独立级联模型的级联推断问题，并从复杂性到算法进行了全面处理：(a) 我们提出了连贯树的概念作为级联的推断结构；这些树通过满足观测时间信息约束的路径连接源节点和观测节点。 (b) 我们引入了度量标准来衡量连贯树作为推断级联的可能性，以及几个用于寻找它们的优化问题。 (c) 我们表明，连贯树的决策问题是普遍NP难的，而优化问题难以近似。 (d) 我们提供了具有推断级联质量性能保证的近似算法，以及启发式方法。 我们通过真实和合成数据实验验证了我们的推断算法的效率和有效性。 显示英文摘要

摘要： In social networks, information and influence diffuse among users as cascades. While the importance of studying cascades has been recognized in various applications, it is difficult to observe the complete structure of cascades in practice. Moreover, much less is known on how to infer cascades based on partial observations. In this paper we study the cascade inference problem following the independent cascade model, and provide a full treatment from complexity to algorithms: (a) We propose the idea of consistent trees as the inferred structures for cascades; these trees connect source nodes and observed nodes with paths satisfying the constraints from the observed temporal information. (b) We introduce metrics to measure the likelihood of consistent trees as inferred cascades, as well as several optimization problems for finding them. (c) We show that the decision problems for consistent trees are in general NP-complete, and that the optimization problems are hard to approximate. (d) We provide approximation algorithms with performance guarantees on the quality of the inferred cascades, as well as heuristics. We experimentally verify the efficiency and effectiveness of our inference algorithms, using real and synthetic data.