大标号二部图的异常检测与校正
Anomaly Detection and Correction in Large Labeled Bipartite Graphs
摘要：
二进制分类问题可以自然地建模为二部图，其中我们试图根据它们的左邻接对右节点进行分类。我们考虑的情况下，标记的二部图，其中一些标签和边是不可信的。我们的目标是通过识别和固定这些标签和边缘来减少噪音。首先，我们提出了一种几何技术，用于生成随机图实例与不可信的标签，并分析所产生的图形属性。我们专注于生成反映真实世界数据的图形，其中度和标签频率遵循幂律分布。我们回顾了几个算法的问题的检测和校正，提出新的扩展和观察特定的二分的情况下。这些算法的范围从数学编程算法到离散组合算法，再到贝叶斯近似算法，再到机器学习算法。我们使用几个指标比较所有这些算法的性能，并根据我们的观察，确定每个单独的算法的相对优势和劣势。
英文摘要：
Binary classification problems can be naturally modeled as bipartite graphs, where we attempt to classify right nodes based on their left adjacencies. We consider the case of labeled bipartite graphs in which some labels and edges are not trustworthy. Our goal is to reduce noise by identifying and fixing these labels and edges. We first propose a geometric technique for generating random graph instances with untrustworthy labels and analyze the resulting graph properties. We focus on generating graphs which reflect real-world data, where degree and label frequencies follow power law distributions. We review several algorithms for the problem of detection and correction, proposing novel extensions and making observations specific to the bipartite case. These algorithms range from math programming algorithms to discrete combinatorial algorithms to Bayesian approximation algorithms to machine learning algorithms. We compare the performance of all these algorithms using several metrics and, based on our observations, identify the relative strengths and weaknesses of each individual algorithm.
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