Software defect reports are an important type of text data for enterprises as they provide actionable information for improving software quality. Identifying the software defect type automatically can greatly enhance and expedite defect management. Class imbalance is a real-life problem in enterprise software defect classification task and adversely affects the automation effort. We show that zero shot learning based technique can be a good alternative to the well-known supervised learning and SMOTE techniques.

Graph convolutional networks (GCNs) have achieved huge success in several machine learning (ML) tasks on graph-structured data. Recently, several sampling techniques have been proposed for the efficient training of GCNs and to improve the performance of GCNs on ML tasks. Specifically, the subgraph-based sampling approaches such as ClusterGCN and GraphSAINT have achieved state-of-the-art performance on the node classification tasks. These subgraph-based sampling approaches rely on heuristics - such as graph partitioning via edge cuts - to identify clusters that are then treated as minibatches during GCN training. In this work, we hypothesize that rather than relying on such heuristics, one can learn a reinforcement learning (RL) policy to compute efficient clusters that lead to effective GCN performance. To that end, we propose PolicyClusterGCN, an online RL framework that can identify good clusters for GCN training. We develop a novel Markov Decision Process (MDP) formulation that allows the policy network to predict "importance" weights on the edges which are then utilized by a clustering algorithm (Graclus) to compute the clusters. We train the policy network using a standard policy gradient algorithm where the rewards are computed from the classification accuracies while training GCN using clusters given by the policy. Experiments on six real-world datasets and several synthetic datasets show that PolicyClusterGCN outperforms existing state-of-the-art models on node classification task.

Traffic signal control is an important problem in urban mobility with a significant potential for economic and environmental impact. While there is a growing interest in Reinforcement Learning (RL) for traffic signal control, the work so far has focussed on learning through simulations which could lead to inaccuracies due to simplifying assumptions. Instead, real experience data on traffic is available and could be exploited at minimal costs. Recent progress in offline or batch RL has enabled just that. Model-based offline RL methods, in particular, have been shown to generalize from the experience data much better than others.

We study the weak supervision learning problem of Learning from Label Proportions (LLP) where the goal is to learn an instance-level classifier using proportions of various class labels in a bag -- a collection of input instances that often can be highly correlated. While representation learning for weakly-supervised tasks is found to be effective, they often require domain knowledge. To the best of our knowledge, representation learning for tabular data (unstructured data containing both continuous and categorical features) are not studied. In this paper, we propose to learn diverse representations of instances within the same bags to effectively utilize the weak bag-level supervision. We propose a domain agnostic LLP method, called "Self Contrastive Representation Learning for LLP" (SelfCLR-LLP) that incorporates a novel self--contrastive function as an auxiliary loss to learn representations on tabular data for LLP. We show that diverse representations for instances within the same bags aid efficient usage of the weak bag-level LLP supervision. We evaluate the proposed method through extensive experiments on real-world LLP datasets from e-commerce applications to demonstrate the effectiveness of our proposed SelfCLR-LLP. In this paper, we propose to learn diverse representations of instances within the same bags to effectively utilize the weak bag-level supervision. We propose a domain agnostic LLP method, called "Self Contrastive Representation Learning for LLP" (SelfCLR-LLP) that incorporates a novel self--contrastive function as an auxiliary loss to learn representations on tabular data for LLP. We show that diverse representations for instances within the same bags aid efficient usage of the weak bag-level LLP supervision. We evaluate the proposed method through extensive experiments on real-world LLP datasets from e-commerce applications to demonstrate the effectiveness of our proposed SelfCLR-LLP.

In this work, we present a novel approach for link prediction on heterogeneous networks – networks that accommodate multiple types of nodes as well as multiple types of relations among them. Specifically, we propose a multi-view network representation learning framework to incorporate structural intuitions from the underlying graph and enrich the relational representations for link prediction. The method relies on the metapath view, the community view, and the subgraph view between a source and target node pair whose linkage is to be predicted. Furthermore, our proposed model leverages a relation-aware attention mechanism to aggregate the candidate contexts in a principled way. Empirically, we demonstrate that the proposed architecture outperforms state-of-the-art transductive and inductive methods in link prediction by a significant margin. A detailed ablation study and attention weight visualizations suggest that the chosen views are complementary and useful to predict links robustly.

We formulate a new inference task in the domain of multivariate time series forecasting (MTSF), called Variable Subset Forecast (VSF), where only a small subset of the variables is available during inference. Variables are absent during inference because of long-term data loss (eg. sensor failures) or high -> low-resource domain shift between train / test. To the best of our knowledge, robustness of MTSF models in presence of such failures, has not been studied in the literature. Through extensive evaluation, we first show that the performance of state of the art methods degrade significantly in the VSF setting. We propose a non-parametric, wrapper technique that can be applied on top any existing forecast models. Through systematic experiments across 4 datasets and 5 forecast models, we show that our technique is able to recover close to 95% performance of the models even when only 15% of the original variables are present.

Multiplex networks are complex graph structures in which a set of entities are connected to each other via multiple types of relations, each relation representing a distinct layer. Such graphs are used to investigate many complex biological, social, and technological systems. In this work, we present a novel semi-supervised approach for structure-aware representation learning on multiplex networks. Our approach relies on maximizing the mutual information between local node-wise patch representations and label correlated structure-aware global graph representations to model the nodes and cluster structures jointly. Specifically, it leverages a novel cluster-aware, node-contextualized global graph summary generation strategy for effective joint-modeling of node and cluster representations across the layers of a multiplex network. Empirically, we demonstrate that the proposed architecture outperforms state-of-the-art methods in a range of tasks: classification, clustering, visualization, and similarity search on seven real-world multiplex networks for various experiment settings.

We propose a Semi-Supervised Learning (SSL) methodology that explicitly encodes different necessary priors to learn efficient representations for nodes in a network. The key to our framework is a semi-supervised cluster invariance constraint that explicitly groups nodes of similar labels together. We show that explicitly encoding this constraint allows one to learn meaningful node representations from both qualitative (visual) and quantitative standpoints. Specifically, our methodology realizes improved node classification and visually-enhanced clusterability of nodes on a wide range of datasets over competitive baselines.

SimRank is a widely studied link-based similarity measure that is known for its simple, yet powerful philosophy that two nodes are similar if they are referenced by similar nodes. While this philosophy has been the basis of several improvements, there is another useful, albeit less frequently discussed interpretation for SimRank known as the Random Surfer-Pair Model. In this work, we show that other well known measures related to SimRank can also be reinterpreted using Random Surfer-Pair Models, and establish a mathematically sound, general and unifying framework for several link-based similarity measures. This also serves to provide new insights into their functioning and allows for using these measures in a Monte Carlo framework, which provides several computational benefits. As an illustration of its utility in designing measures, we develop a new measure based on two existing measures under this framework, and empirically demonstrate its efficacy.

Graphs are increasingly becoming ubiquitous as models for structured data. A generative model that closely mimics the structural properties of a given set of graphs has utility in a variety of domains. Much of the existing work require that a large number of parameters, in fact exponential in size of the graphs, be estimated from the data. We take a slightly different approach to this problem, leveraging the extensive prior work in the formal graph grammar literature. In this paper, we propose a graph generation model based on Probabilistic Edge Replacement Grammars (PERGs). We propose a variant of PERG called Restricted PERG (RPERG), which is analogous to PCFGs in string grammar literature. With this restriction, we are able to derive a learning algorithm for estimating the parameters of the grammar from graph data. We empirically demonstrate on real life datasets that RPERGs outperform existing methods for graph generation. We improve on the performance of the state-of-the-art Hyperedge Replacement Grammar based graph generative model. Despite being a context free grammar, the proposed model is able to capture many of the structural properties of real networks, such as degree distributions, power law and spectral characteristics.

Community detection is one of the most well known problems in complex network analysis. In real-world networks, communities often overlap. Various approaches have been proposed in the literature to detect overlapping communities in networks. Local Expansion and optimization approaches have gained popularity due to their scalability and robustness. In a method based on local expansion, the seeding strategy and scoring function employed are crucial to the performance of the algorithm.

In social network analysis, the fundamental idea behind the notion of roles is to discover actors who have similar structural signatures. Actors performing the same role have similar behavioural and functional characteristics. Few examples of structural roles are bridge nodes, clique members and star centers. Role discovery involves partitioning the nodes in a network based on their structural characteristics. The notion of roles is complementary to the notion of community detection, which involves partitioning the network into cohesive subgroups. In this paper we propose a novel algorithm RID"Rs (Role Identification and Discovery using "-equitable Refinements): a graph partitioning approach for extracting soft roles in networks. RID"Rs discovers structural roles based on the global graph characteristics of a network. Evaluating the quality of roles discovered is nontrivial due to the lack of ground-truth role datasets, we present a novel framework for evaluating and comparing various role discovery approaches. We also demonstrate the e ectiveness of RID"Rs on diverse graph mining tasks: role identification/discovery and for finding top-k nodes that are most similar to a given node. Further, the empirical scalability analysis of our proposed algorithm on random power-law graphs shows that our approach is highly scalable.

Factorization Machine (FM) provides a generic framework that combines the prediction quality of factorization models with the flexibility of feature engineering that discriminative models like SVM offer. The Bayesian Factorization Machine [11], with its impressive predictive performance and the convenience of automatic tuning of parameters, has been one of the most successful and efficient approaches within this framework. However, this model has two major drawbacks. Firstly, it assumes that the data is generated from Gaussian distributions that may not be the best assumption for count data such as integer-valued ratings. Secondly, to get the best performance, one needs to cross-validate over the number of latent factors used for modeling the pairwise interaction in FM, a process that is computationally intensive. This paper introduces the Nonparametric Poisson Factorization Machine (NPFM), which models count data using the Poisson distribution, which provides both modeling and computational advantages for sparse data. The ideal number of latent factors is estimated from the data itself, thereby addressing a key limitation of existing approaches to FM. Additionally, NPFM has linear time complexity with respect to the number of non-zero observations.

A fundamental problem in behavioral analysis of human interactions is to understand how communications unfold. In this paper, we study this problem by mining Communication motifs from dynamic interaction networks. A communication motif is a recurring subgraph that has a similar sequence of information flow. Mining communication motifs requires us to explore the exponential subgraph search space where existing techniques fail to scale. To tackle this scalability bottleneck, we develop a technique called COMMIT. COMMIT converts a dynamic graph into a database of sequences. Through careful analysis in the sequence space, only a small portion of the exponential search space is accessed to identify regions embedding communication motifs. Extensive experiments on three different social networks show COMMIT to be up to two orders of magnitude faster than baseline techniques. Furthermore, qualitative analysis demonstrate communication motifs to be effective in characterizing the recurring patterns of interactions while also revealing the role that the underlying social network plays in shaping human behavior.

Classical machine learning techniques assume the data to be i.i.d., but the real world data is inherently relational and can generally be represented using graphs or some variants of a graph representation. The importance of modeling relational data is evident from its increasing presence in many domains: Telecom networks, WWW, social networks, organizational networks, images, protein sequences, etc. This field has recently been receiving a lot of attention in various communities under different themes depending on the problem addressed and the nature of solution proposed. Collective classification is one such popular approach which involves the use of a local classifier that embeds the node's own attributes and neighbors' information in a feature vector, and classifies the nodes in an iterative procedure. Despite the increasing popularity, there is not much attention paid towards datasets with multiple attributes and multi-relational (MAMR) networks under multi-label scenarios. In MAMR data, nodes can be represented using multiple types of attributes (attribute views) and there are multiple link types between the nodes. For example, in Twitter, users can be represented using their tweets, urls shared, hashtags and list memberships. And different Twitter users can be connected using follower, followed by and re-tweet links. Secondly, in many networks, nodes are associated with more than one label. For instance, Twitter users can be tagged with one or more labels from a set L, where L contains various movie genres that a user might like. Motivated by this, we propose a learning technique for multi-label collective classification using multiple attribute views on multi-relational network data which captures complex label correlations within and across attribute/relationship types. We empirically evaluate our proposed approach on Twitter and MovieLens datasets, and we show that it performs better than the state-of-art approaches.

Multi-Document Summarization deals with computing a summary for a set of related articles such that they give the user a general view about the events. One of the objectives is that the sentences should cover the different events in the documents with the information covered in as few sentences as possible. Latent Dirichlet Allocation can breakdown these documents into different topics or events. However to reduce the common information content the sentences of the summary need to be orthogonal to each other since orthogonal vectors have the lowest possible similarity and correlation between them. Singular Value Decompositions used to get the orthogonal representations of vectors and representing sentences as vectors, we can get the sentences that are orthogonal to each other in the LDA mixture model weighted term domain. Thus using LDA we find the different topics in the documents and using SVD we find the sentences that best represent these topics. Finally we present the evaluation of the algorithms on the DUC2002 Corpus multi-document summarization tasks using the ROUGE evaluator to evaluate the summaries. Compared to DUC 2002 winners, our algorithms gave significantly better ROUGE-1 recall measures.