Co-evolving sequences are ubiquitous in a variety of applications, where different sequences are often inherently inter-connected with each other. We refer to such sequences, together with their inherent connections modeled as a structured network, as network of co-evolving sequences (NoCES). Typical NoCES applications include road traffic monitoring, company revenue prediction, motion capture, etc. To date, it remains a daunting challenge to accurately model NoCES due to the coupling between network structure and sequences. In this paper, we propose to modeling \pname\ with the aim of simultaneously capturing both the dynamics and the interplay between network structure and sequences. Specifically, we propose a joint learning framework to alternatively update the network representations and sequence representations as the sequences evolve over time. A unique feature of our framework lies in that it can deal with the case when there are co-evolving sequences on both network nodes and edges. Experimental evaluations on four real datasets demonstrate that the proposed approach (1) outperforms the existing competitors in terms of prediction accuracy, and (2) scales linearly w.r.t. the sequence length and the network size.

Learning node representations in a network has a wide range of applications. Most of the existing work focuses on improving the performance of the learned node representations by designing advanced network embedding models. In contrast to these work, this article aims to provide some understanding of the rationale behind the existing network embedding models, e.g.,whya given embedding algorithm outputs the specific node representations andhowthe resulting node representations relate to the structure of the input network. In particular, we propose to discern the edge influence for two widely-studied classes of network embedding models, i.e., skip-gram based models and graph neural networks. We provide algorithms to effectively and efficiently quantify the edge influence on node representations, and further identify high-influential edges by exploiting the linkage between edge influence and network structure. Experimental evaluations are conducted on real datasets showing that: 1) in terms of quantifying edge influence, the proposed method is significantly faster (up to$2,000\times$) than straightforward methods with little quality loss, and 2) in terms of identifying high-influential edges, the identified edges by the proposed method have a significant impact in the context of downstream prediction task and adversarial attacking.

Network embedding aims to automatically learn the node representations in networks. The basic idea of network embedding is to first construct a network to describe the neighborhood context for each node, and then learn the node representations by designing an objective function to preserve certain properties of the constructed context network. The vast majority of the existing methods, explicitly or implicitly, follow a pointwise design principle. That is, the objective can be decomposed into the summation of the certain goodness function over each individual edge of the context network. In this paper, we propose to go beyond such pointwise approaches, and introduce the ranking-oriented design principle for network embedding. The key idea is to decompose the overall objective function into the summation of a goodness function over a set of edges to collectively preserve their relative rankings on the context network. We instantiate the ranking-oriented design principle by two new network embedding algorithms, including a pairwise network embedding method PaWine which optimizes the relative weights of edge pairs, and a listwise method LiWine which optimizes the relative weights of edge lists. Both proposed algorithms bear a linear time complexity, making themselves scalable to large networks. We conduct extensive experimental evaluations on five real datasets with a variety of downstream learning tasks, which demonstrate that the proposed approaches consistently outperform the existing methods.

Network embedding, which learns the low-dimensional representations of nodes, has gained significant research attention. Despite its superior empirical success, often measured by the prediction performance of downstream tasks (e.g., multi-label classification), it is unclear \em why a given embedding algorithm outputs the specific node representations, and \em how the resulting node representations relate to the structure of the input network. In this paper, we propose to discern the edge influence as the first step towards understanding skip-gram basd network embedding methods. For this purpose, we propose an auditing framework Near, whose key part includes two algorithms (Near-add \ and Near-del ) to effectively and efficiently quantify the influence of each edge. Based on the algorithms, we further identify high-influential edges by exploiting the linkage between edge influence and the network structure. Experimental results demonstrate that the proposed algorithms (Near-add \ and Near-del ) are significantly faster (up to $2,000\times$) than straightforward methods with little quality loss. Moreover, the proposed framework can efficiently identify the most influential edges for network embedding in the context of downstream prediction task and adversarial attacking.

Bug tracking systems, which help to track the reported software bugs, have been widely used in software development and maintenance. In these systems, recognizing relevant source files among a large number of source files for a given bug report is a time-consuming and labor-intensive task for software developers. To tackle this problem, information retrieval methods have been widely used to capture either the textual similarities or the semantic similarities between bug reports and source files. However, these two types of similarities are usually considered separately and the historical bug fixings are largely ignored by the existing methods. In this paper, we propose a supervised topic modeling method (STMLOCATOR) for automatically locating the relevant source files for a given bug report. In particular, the proposed model is built upon three key observations. First, supervised modeling can effectively make use of the existing fixing histories. Second, certain words in bug reports tend to appear multiple times in their relevant source files. Third, longer source files tend to have more bugs. By integrating the above three observations, the proposed STMLOCATOR utilizes historical fixings in a supervised way and learns both the textual similarities and semantic similarities between bug reports and source files. We further consider a special type of bug reports with stack-traces in bug reports, and propose a variant of STMLOCATOR to tailor for such bug reports. Experimental evaluations on three real data sets demonstrate that the proposed STMLOCATOR can achieve up to 23.6% improvement in terms of prediction accuracy over its best competitors, and scales linearly with the size of the data. Moreover, the proposed variant further improves STMLOCATOR by up to 76.2% on those bug reports with stack-traces.

Information retrieval methods have been proposed to help developers locate related buggy source files for a given bug report. The basic assumption of these methods is that the bug description in a bug report should be textually similar to its buggy source files. However, the metadata (such as the component and version information) in bug reports is largely ignored by these methods. In this paper, we propose to explore the metadata for the bug localization task. In particular, we first apply a generative model to locate buggy source files based on the bug descriptions, and then propose to add the available metadata in bug reports into the localization process. Experimental evaluations on several software projects indicate that the metadata is useful to improve the localization accuracy and that the proposed bug localization method outperforms several existing methods.

With the great popularity of mobile devices, the amount of mobile apps has grown at a more dramatic rate than ever expected. A technical challenge is how to recommend suitable apps to mobile users. In this work, we identify and focus on a unique characteristic that exists in mobile app recommendation—that is, an app usually corresponds to multiple release versions. Based on this characteristic, we propose a fine-grain version-aware app recommendation problem. Instead of directly learning the users’ preferences over the apps, we aim to infer the ratings of users on a specific version of an app. However, the user-version rating matrix will be sparser than the corresponding user-app rating matrix, making existing recommendation methods less effective. In view of this, our approach has made two major extensions. First, we leverage the review text that is associated with each rating record; more importantly, we consider two types of version-based correlations. The first type is to capture the temporal correlations between multiple versions within the same app, and the second type of correlation is to capture the aggregation correlations between similar apps. Experimental results on a large dataset demonstrate the superiority of our approach over several competitive methods.

During an Open Source Software (OSS) maintenance, bug localization is a laborsome and time-consuming work for geographically-separated developers. It is desirable to automatically identify related buggy files once a bug report is submitted. Existing work has proposed information retrieval techniques for this problem. However, these proposals tend to neglect the inherent structure in bug localization and they are largely dependent on the comments (annotations) in source files which may be unavailable. In this paper, we propose a random walk based approach to detecting buggy source files based on bug reports. In particular, we separately process source files and bug reports to make it less sensitive to comments, and then apply random walk to capture the inherent structure in bug localization. Experimental evaluations on three real-world open-source projects demonstrate that the proposed approach can outperform several existing methods and that it is less dependent on the comments in source files.