85 research outputs found

    A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions

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    Graphs represent interconnected structures prevalent in a myriad of real-world scenarios. Effective graph analytics, such as graph learning methods, enables users to gain profound insights from graph data, underpinning various tasks including node classification and link prediction. However, these methods often suffer from data imbalance, a common issue in graph data where certain segments possess abundant data while others are scarce, thereby leading to biased learning outcomes. This necessitates the emerging field of imbalanced learning on graphs, which aims to correct these data distribution skews for more accurate and representative learning outcomes. In this survey, we embark on a comprehensive review of the literature on imbalanced learning on graphs. We begin by providing a definitive understanding of the concept and related terminologies, establishing a strong foundational understanding for readers. Following this, we propose two comprehensive taxonomies: (1) the problem taxonomy, which describes the forms of imbalance we consider, the associated tasks, and potential solutions; (2) the technique taxonomy, which details key strategies for addressing these imbalances, and aids readers in their method selection process. Finally, we suggest prospective future directions for both problems and techniques within the sphere of imbalanced learning on graphs, fostering further innovation in this critical area.Comment: The collection of awesome literature on imbalanced learning on graphs: https://github.com/Xtra-Computing/Awesome-Literature-ILoG

    Learning Adaptive Representations for Image Retrieval and Recognition

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    Content-based image retrieval is a core problem in computer vision. It has a wide range of application such as object and place recognition, digital library search, organizing image collections, and 3D reconstruction. However, robust and accurate image retrieval from a large-scale image collection still remains an open problem. For particular instance retrieval, challenges come not only from photometric and geometric changes between the query and the database images, but also from severe visual overlap with irrelevant images. On the other hand, large intra-class variation and inter-class similarity between semantic categories represents a major obstacle in semantic image retrieval and recognition. This dissertation explores learning image representations that adaptively focus on specific image content to tackle these challenges. For this purpose, three kinds of image contexts for discriminating relevant and irrelevant image content are exploited: (1) local image context, (2) semi-global image context, and (3) global image context. Novel models for learning adaptive image representations based on each context are introduced. Moreover, as a byproduct of training the proposed models, the underlying task-relevant contexts are automatically revealed from the data in a self-supervised manner. These include data-driven notion of good local mid-level features, task-relevant semi-global contexts with rich high-level information, and the hierarchy of images. Experimental evaluation illustrates the superiority of the proposed methods in the applications of place recognition, scene categorization, and particular object retrieval.Doctor of Philosoph

    Class-Imbalanced Learning on Graphs: A Survey

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    The rapid advancement in data-driven research has increased the demand for effective graph data analysis. However, real-world data often exhibits class imbalance, leading to poor performance of machine learning models. To overcome this challenge, class-imbalanced learning on graphs (CILG) has emerged as a promising solution that combines the strengths of graph representation learning and class-imbalanced learning. In recent years, significant progress has been made in CILG. Anticipating that such a trend will continue, this survey aims to offer a comprehensive understanding of the current state-of-the-art in CILG and provide insights for future research directions. Concerning the former, we introduce the first taxonomy of existing work and its connection to existing imbalanced learning literature. Concerning the latter, we critically analyze recent work in CILG and discuss urgent lines of inquiry within the topic. Moreover, we provide a continuously maintained reading list of papers and code at https://github.com/yihongma/CILG-Papers.Comment: submitted to ACM Computing Survey (CSUR

    QUERY-SPECIFIC SUBTOPIC CLUSTERING IN RESPONSE TO BROAD QUERIES

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    Information Retrieval (IR) refers to obtaining valuable and relevant information from various sources in response to a specific information need. For the textual domain, the most common form of information sources is a collection of textual documents or text corpus. Depending on the scope of the information need, also referred to as the query, the relevant information can span a wide range of topical themes. Hence, the relevant information may often be scattered through multiple documents in the corpus, and each satisfies the information need to varying degrees. Traditional IR systems present the relevant set of documents in the form of a ranking where the rank of a particular document corresponds to its degree of relevance to the query. If the query is sufficiently specific, the set of relevant documents will be more or less about similar topics. However, they will be much more topically diverse when the query is vague or about a generalized topic, e.g., ``Computer science. In such cases, multiple documents may be of equal importance as each represents a specific facade of the broad topic of the query. Consider, for example, documents related to information retrieval and machine learning for the query ``Computer Science. In this case, the decision to rank documents from these two subtopics would be ambiguous. Instead, presenting the retrieved results as a cluster of documents where each cluster represents one subtopic would be more appropriate. Subtopic clustering of search results has been explored in the domain of Web-search, where users receive relevant clusters of search results in response to their query. This thesis explores query-specific subtopic clustering that incorporates queries into the clustering framework. We develop a query-specific similarity metric that governs a hierarchical clustering algorithm. The similarity metric is trained to predict whether a pair of relevant documents should also share the same subtopic cluster in the context of the query. Our empirical study shows that direct involvement of the query in the clustering model significantly improves the clustering performance over a state-of-the-art neural approach on two publicly available datasets. Further qualitative studies provide insights into the strengths and limitations of our proposed approach. In addition to query-specific similarity metrics, this thesis also explores a new supervised clustering paradigm that directly optimizes for a clustering metric. Being discrete functions, existing approaches for supervised clustering find it difficult to use a clustering metric for optimization. We propose a scalable training strategy for document embedding models that directly optimizes for the RAND index, a clustering quality metric. Our method outperforms a strong neural approach and other unsupervised baselines on two publicly available datasets. This suggests that optimizing directly for the clustering outcome indeed yields better document representations suitable for clustering. This thesis also studies the generalizability of our findings by incorporating the query-specific clustering approach and our clustering metric-based optimization technique into a single end-to-end supervised clustering model. Also, we extend our methods to different clustering algorithms to show that our approaches are not dependent on any specific clustering algorithm. Having such a generalized query-specific clustering model will help to revolutionize the way digital information is organized, archived, and presented to the user in a context-aware manner

    Toward Sustainable Recommendation Systems

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    Recommendation systems are ubiquitous, acting as an essential component in online platforms to help users discover items of interest. For example, streaming services rely on recommendation systems to serve high-quality informational and entertaining content to their users, and e-commerce platforms recommend interesting items to assist customers in making shopping decisions. Further-more, the algorithms and frameworks driving recommendation systems provide the foundation for new personalized machine learning methods that have wide-ranging impacts. While successful, many current recommendation systems are fundamentally not sustainable: they focus on short-lived engagement objectives, requiring constant fine-tuning to adapt to the dynamics of evolving systems, or are subject to performance degradation as users and items churn in the system. In this dissertation research, we seek to lay the foundations for a new class of sustainable recommendation systems. By sustainable, we mean a recommendation system should be fundamentally long-lived, while enhancing both current and future potential to connect users with interesting content. By building such sustainable recommendation systems, we can continuously improve the user experience and provide a long-lived foundation for ongoing engagement. Building on a large body of work in recommendation systems, with the advance in graph neural networks, and with recent success in meta-learning for ML-based models, this dissertation focuses on sustainability in recommendation systems from the following three perspectives with corresponding contributions: • Adaptivity: The first contribution lies in capturing the temporal effects from the instant shifting of users’ preferences to the lifelong evolution of users and items in real-world scenarios, leading to models which are highly adaptive to the temporal dynamics present in online platforms and provide improved item recommendation at different timestamps. • Resilience: Secondly, we seek to identify the elite users who act as the “backbone” recommendation systems shape the opinions of other users via their public activities. By investigating the correlation between user’s preference on item consumption and their connections to the “backbone”, we enable recommendation models to be resilient to dramatic changes including churn in new items and users, and frequently updated connections between users in online communities. • Robustness: Finally, we explore the design of a novel framework for “learning-to-adapt” to the imperfect test cases in recommendation systems ranging from cold-start users with few interactions to casual users with low activity levels. Such a model is robust to the imperfection in real-world environments, resulting in reliable recommendation to meet user needs and aspirations
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