Community structures are critical towards understanding not only the network topology but also how the network functions. However, how to evaluate the quality of detected community structures is still challenging and remains unsolved. The most widely used metric, normalized mutual information (NMI), was proven to have finite size effect, and its improved form relative normalized mutual information (rNMI) has reverse finite size effect. Corrected normalized mutual information (cNMI) was thus proposed and has neither finite size effect nor reverse finite size effect. However, in this paper, we show that cNMI violates the so-called proportionality assumption. In addition, NMI-type metrics have the problem of ignoring importance of small communities. Finally, they cannot be used to evaluate a single community of interest. In this paper, we map the computed community labels to the ground-truth ones through integer linear programming, and then use kappa index and F-score to evaluate the detected community structures. Experimental results demonstrate the advantages of our method.

Evaluation of Community Detection Methods

This paper presents a robust and efficient method for tracking topological features in time-varying scalar data. Structures are tracked based on the optimal matching between persistence diagrams with respect to the Wasserstein metric. This fundamentally relies on solving the assignment problem, a special case of optimal transport, for all consecutive timesteps. Our approach relies on two main contributions. First, we revisit the seminal assignment algorithm by Kuhn and Munkres which we specifically adapt to the problem of matching persistence diagrams in an efficient way. Second, we propose an extension of the Wasserstein metric that significantly improves the geometrical stability of the matching of domain-embedded persistence pairs. We show that this geometrical lifting has the additional positive side-effect of improving the assignment matrix sparsity and therefore computing time. The global framework computes persistence diagrams and finds optimal matchings in parallel for every consecutive timestep. Critical trajectories are constructed by associating successively matched persistence pairs over time. Merging and splitting events are detected with a geometrical threshold in a post-processing stage. Extensive experiments on real-life datasets show that our matching approach is up to two orders of magnitude faster than the seminal Munkres algorithm. Moreover, compared to a modern approximation method, our approach provides competitive runtimes while guaranteeing exact results. We demonstrate the utility of our global framework by extracting critical point trajectories from various time-varying datasets and compare it to the existing methods based on associated overlaps of volumes. Robustness to noise and temporal resolution downsampling is empirically demonstrated.

Lifted Wasserstein Matcher for Fast and Robust Topology Tracking

Security patches, embedding both vulnerable code and the corresponding fixes, are of great significance to vulnerability detection and software maintenance. However, the existing patch datasets suffer from insufficient samples and low varieties. In this paper, we construct a large-scale patch dataset called PatchDB that consists of three components, namely, NVD-based dataset, wild-based dataset, and synthetic dataset. The NVD-based dataset is extracted from the patch hyperlinks indexed by the NVD. The wild-based dataset includes security patches that we collect from the commits on GitHub. To improve the efficiency of data collection and reduce the effort on manual verification, we develop a new nearest link search method to help find the most promising security patch candidates. Moreover, we provide a synthetic dataset that uses a new oversampling method to synthesize patches at the source code level by enriching the control flow variants of original patches. We conduct a set of studies to investigate the effectiveness of the proposed algorithms and evaluate the properties of the collected dataset. The experimental results show that PatchDB can help improve the performance of security patch identification.

PatchDB: A Large-Scale Security Patch Dataset

Probabilistic record linkage (PRL) is the process of determining which records in two databases correspond to the same underlying entity in the absence of a unique identifier. Bayesian solutions to this problem provide a powerful mechanism for propagating uncertainty due to uncertain links between records (via the posterior distribution). However, computational considerations severely limit the practical applicability of existing Bayesian approaches. We propose a new computational approach, providing both a fast algorithm for deriving point estimates of the linkage structure that properly account for one-to-one matching and a restricted MCMC algorithm that samples from an approximate posterior distribution. Our advances make it possible to perform Bayesian PRL for larger problems, and to assess the sensitivity of results to varying prior specifications. We demonstrate the methods on a subset of an OCR'd dataset, the California Great Registers, a collection of 57 million voter registrations from 1900 to 1968 that comprise the only panel data set of party registration collected before the advent of scientific surveys.

Scaling Bayesian Probabilistic Record Linkage with Post-Hoc Blocking: An Application to the California Great Registers

With the growth of computing power, deep learning methods have recently been widely used in machine fault diagnosis. In order to realize highly efficient diagnosis accuracy, people need to know the detailed health condition of collected signals from equipment. However, in the actual situation, it is costly and time-consuming to close down machines and inspect components. This seriously impedes the practical application of data-driven diagnosis. In comparison, the full-labeled machine signals from test rigs or online datasets can be achieved easily, which is helpful for the diagnosis of real equipment. Thus, we introduced an improved Wasserstein distance-based transfer learning method (WDA), which learns transferable features between labeled and unlabeled signals from different forms of equipment. In WDA, Wasserstein distance with cosine similarity is applied to narrow the gap between signals collected from different machines. Meanwhile, we use the Kuhn-Munkres algorithm to calculate the Wasserstein distance. In order to further verify the proposed method, we developed a set of case studies, including two different mechanical parts, five transfer scenarios, and eight transfer learning fault diagnosis experiments. WDA reached an average accuracy of 93.72% in bearing fault diagnosis and 84.84% in ball screw fault diagnosis, which greatly surpasses state-of-the-art transfer learning fault diagnosis methods. In addition, comprehensive analysis and feature visualization are also presented.

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WDA: An Improved Wasserstein Distance-Based Transfer Learning Fault Diagnosis Method

Kuhn-Munkres algorithm is one of the most popular polynomial time algorithms for solving clas- sical assignment problem. The assignment problem is to find a n a ssignment o f t he j obs t o t he w orkers that has minimum cost, given a cost matrix X 2 R mn , where the element in the i-th row and j-th column rep- resents the cost of assigning the i-th job to the j-th worker. the time complexity of Kuhn-Munkres algorithm is O(mn 2 ), which brings prohibitive computational burden on large scale matrices, limiting the further usage of these methods in real applications. Motivated by this observation, a series of acceleration skills and paral- lel techniques have been studied on special structure. In this paper, we improve the original Kuhn-Munkres algorithm by utilizing the sparsity structure of the cost matrix, and propose two algorithms, sparsity based KM(sKM) and parallel KM(pKM). Furthermore, numerical experiments are given to show the efficiency of our algorithm. We empirically evaluate the proposed algorithm sKM) and (pKM) on random generated largescale datasets. Results have shown that sKM) greatly improves the computational performance. At the same time, (pKM) provides a parallel way to solve assignment problem with considerable accuracy loss.

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Qinyu Chen

Papers

Solving large-scale assignment problems by Kuhn-Munkres algorithm