Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors significantly differ from normal ones, given only a limited number of annotated anomalies and abundant unlabeled samples. A major challenge is to learn a meaningful latent feature representation that reduces intra-class variance among normal data while remaining highly sensitive to anomalies. Although recent works have applied self-supervised feature learning for graph anomaly detection, their strategies are not specifically tailored to its unique requirements, motivating our exploration of a more domain-specific approach. In this paper, we introduce a weakly supervised graph anomaly detection method that leverages a feature learning strategy tailored for graph anomalies. Our approach is built upon a multi-task learning scheme that extracts robust feature representations through synthesized anomalies. We generate synthetic anomalies by perturbing the normal graph in various ways and assign a dedicated detection head to each anomaly type, ensuring that learned features are sensitive to potential deviations from normal patterns. Although synthetic anomalies may not perfectly replicate real-world patterns, they provide valuable auxiliary data for effective feature learnin, much like features learned from ImageNet classification transfer to downstream vision tasks. Additionally, we adopt a two-phase learning strategy: an initial warm-up phase using only synthetic samples, followed by a full-training phase integrating both tasks, to balance the influence of synthetic and real data. Extensive experiments on public datasets demonstrate the superior performance of our method over its competitors. Code is available at https://github.com/yj-zhou/SAWGAD.

Multi-modal time series analysis has recently emerged as a prominent research area, driven by the increasing availability of diverse data modalities, such as text, images, and structured tabular data from real-world sources. However, effective analysis of multi-modal time series is hindered by data heterogeneity, modality gap, misalignment, and inherent noise. Recent advancements in multi-modal time series methods have exploited the multi-modal context via cross-modal interactions based on deep learning methods, significantly enhancing various downstream tasks. In this tutorial and survey, we present a systematic and up-to-date overview of multi-modal time series datasets and methods. We first state the existing challenges of multi-modal time series analysis and our motivations, with a brief introduction of preliminaries. Then, we summarize the general pipeline and categorize existing methods through a unified cross-modal interaction framework encompassing fusion, alignment, and transference at different levels (i.e., input, intermediate, output), where key concepts and ideas are highlighted. We also discuss the real-world applications of multi-modal analysis for both standard and spatial time series, tailored to general and specific domains. Finally, we discuss future research directions to help practitioners explore and exploit multi-modal time series. The up-to-date resources are provided in the GitHub repository. https://github.com/UConn-DSIS/Multi-modal-Time-Series-Analysis.

Time series data is now pervasive across domains such as healthcare, finance, entertainment, and transportation, driven by advances in sensing technologies that enable continuous data collection. The resulting increase in data volume and complexity poses significant challenges to traditional analysis methods, calling for the development of advanced, interdisciplinary approaches to temporal data mining. This workshop aims to: (1) identify key challenges in learning from time series data, including irregular sampling, spatiotemporal dependencies, and uncertainty quantification; (2) explore recent advances in algorithmic, statistical, theoretical, and systems-based solutions-ranging from classical methods to emerging techniques involving large language models (LLMs); and (3) foster collaboration by highlighting open problems and novel research directions in time series analysis. Bridging theory and practice, the workshop provides a platform for researchers and practitioners from academia, industry, and government to exchange ideas, discuss technical challenges, and showcase practical applications. Contributions from related areas such as AI, machine learning, data science, and statistics are strongly encouraged.

Recently, various contrastive learning techniques have been developed to categorize time series data and have exhibited promising performance for real-world applications. A general paradigm is to utilize appropriate data augmentation methods and construct feasible positive samples such that the encoder can yield robust and discriminative representations by mapping similar data points closer together in the feature space while pushing dissimilar data points farther apart. Despite its efficacy, the fine-grained relative similarity (e.g., rank) information of positive samples is not fully exploited, especially when labeled samples are limited. To this end, we present Rank Supervised Contrastive Learning (RankSCL) to perform time series classification. Different from conventional contrastive learning frameworks, RankSCL augments raw data in a targeted manner in the embedding space and selects more informative positive and negative pairs for the targeted sample. Moreover, a novel rank loss is developed to assign higher weights to more confident positive pairs and lower weights to less confident positive pairs, enabling the encoder to extract the same class's fine-grained information and produce a clear boundary among different classes. Thoroughly empirical studies on 128 UCR and 30 UEA datasets demonstrate that the proposed RankSCL can achieve state-of-the-art performance compared to existing baseline methods. Code is available at: https://github.com/UConn-DSIS/Rank-Supervised-Contrastive-Learning-for-Time-Series-Classification.

Memory replay based techniques have shown great success for continual learning with incrementally accumulated Euclidean data. Directly applying them to continually expanding networks, however, leads to the potential memory explosion problem due to the need to buffer representative nodes and their associated topological neighborhood structures. To this end, we systematically analyze the key challenges in the memory explosion problem, and present a general framework,i.e., Parameter Decoupled Graph Neural Networks (PDGNNs) with Topology-aware Embedding Memory (TEM), to tackle this issue. The proposed framework not only reduces the memory space complexity from O (ndL) to O (n)1: memory budget, d: average node degree, L: the radius of the GNN receptive field, but also fully utilizes the topological information for memory replay. Specifically, PDGNNs decouple trainable parameters from the computation ego-subnetwork viaTopology-aware Embeddings (TEs), which compress ego-subnetworks into compact vectors (i.e., TEs) to reduce the memory consumption. Based on this framework, we discover a unique pseudo-training effect in continual learning on expanding networks and this effect motivates us to develop a novel coverage maximization sampling strategy that can enhance the performance with a tight memory budget. Thorough empirical studies demonstrate that, by tackling the memory explosion problem and incorporating topological information into memory replay, PDGNNs with TEM significantly outperform state-of-the-art techniques, especially in the challenging class-incremental setting.

Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications. Recent advances in Foundation Models (FMs) have fundamentally reshaped the paradigm of model design for time series analysis, boosting various downstream tasks in practice. These innovative approaches often leverage pre-trained or fine-tuned FMs to harness generalized knowledge tailored for time series analysis. This survey aims to furnish a comprehensive and up-to-date overview of FMs for time series analysis. While prior surveys have predominantly focused on either application or pipeline aspects of FMs in time series analysis, they have often lacked an in-depth understanding of the underlying mechanisms that elucidate why and how FMs benefit time series analysis. To address this gap, our survey adopts a methodology-centric classification, delineating various pivotal elements of time-series FMs, including model architectures, pre-training techniques, adaptation methods, and data modalities. Overall, this survey serves to consolidate the latest advancements in FMs pertinent to time series analysis, accentuating their theoretical underpinnings, recent strides in development, and avenues for future exploration.

Time series data has become ubiquitous across various fields such as healthcare, finance, entertainment, and transportation, driven by advancements in sensing technologies that enable continuous monitoring and recording. This growth in data size and complexity presents new challenges for traditional analysis techniques, necessitating the development of advanced, interdisciplinary temporal mining algorithms. The goals of this workshop are to: (1) highlight significant challenges in learning and mining from time series data, such as irregular sampling, spatiotemporal structures, and uncertainty quantification; (2) discuss recent developments in algorithmic, theoretical, statistical, and systems-based approaches for addressing these challenges, including both classical methods and large language models (LLMs); and (3) synergize research efforts by exploring both new and open problems in time series analysis and mining. This workshop will focus on both the theoretical and practical aspects of time series data analysis, providing a platform for researchers and practitioners from academia, government, and industry to discuss potential research directions, critical technical issues, and present solutions for practical applications. Contributions from related fields such as AI, machine learning, data science, and statistics are also included.

Inbound parcel volume forecasting problem (IPVFP) plays an important role in the logistics system as it can facilitate various downstream applications. Despite the fact that a number of time series forecasting techniques have been developed, existing approaches fail to explicitly consider intrinsic characteristics of the logistics system, e.g., parcel transport patterns, operation patterns, and their spatial-temporal dependencies. To this end, we propose a novel hybrid inbound parcel volume forecasting model to analyze the logistic spatial-temporal graph that is constructed based on logistics data and the physical location of logistics stations. The graph includes engineered features such as the transition matrix and modified Dynamic Time Warping (DTW) distance matrix, which accurately depicts the parcel transfer patterns within the system. In addition, it incorporates a dedicated attention mechanism that introduces a novel bit-embedding representation method for integer tokens, enabling to capture of dynamic correlations among different timestamps. Finally, a collaborative module comprising dilated convolution layers and Gated Recurrent Units (GRU) is integrated to capture long-term dependencies. Extensive experiments on real-world data evaluate the effectiveness of the proposed graph and model, demonstrating its superiority over 16 other advanced baseline models. We release our code and data at https://github.com/YelsAlyssa/IPVFP.

Imitation learning that replicates experts' skills via their demonstrations has shown significant success in various decision-making tasks. However, two critical challenges still hinder the deployment of imitation learning techniques in real-world application scenarios. First, existing methods lack the intrinsic interpretability to explicitly explain the underlying rationale of the learned skill and thus making learned policy untrustworthy. Second, due to the scarcity of expert demonstrations from each end user (client), learning a policy based on different data silos is necessary but challenging in privacy-sensitive applications such as finance and healthcare. To this end, we present a privacy-preserved interpretable skill learning framework (FedSkill) that enables global policy learning to incorporate data from different sources and provides explainable interpretations to each local user without violating privacy and data sovereignty. Specifically, our proposed interpretable skill learning model can capture the varying patterns in the trajectories of expert demonstrations, and extract prototypical information as skills that provide implicit guidance for policy learning and explicit explanations in the reasoning process. Moreover, we design a novel aggregation mechanism coupled with the based skill learning model to preserve global information utilization and maintain local interpretability under the federated framework. Thoroughly experiments on three datasets and empirical studies demonstrate that our proposed FedSkill framework not only outperforms state-of-the-art imitation learning methods but also exhibits good interpretability under a federated setting. Our proposed FedSkill framework is the first attempt to bridge the gaps among federated learning, interpretable machine learning, and imitation learning.

Time series data has become pervasive across domains such as finance, transportation, retail, entertainment, and healthcare. This shift towards continuous monitoring and recording, fueled by advancements in sensing technologies, necessitates the development of new tools and solutions. Despite extensive study, the importance of time series analysis continues to increase. However, modern time series data present challenges to existing techniques, including irregular sampling and spatiotemporal structures. Time series mining research is both challenging and rewarding as it connects diverse disciplines and requires interdisciplinary solutions. The goals of this workshop are to (1) highlight the significant challenges that underpin learning and mining from time series data (e.g., irregular sampling, spatiotemporal structure, uncertainty quantification), (2) discuss recent algorithmic, theoretical, statistical, or systems-based developments for tackling these problems, and (3) to synergize the research activities and discuss both new and open problems in time series analysis and mining. In summary, our workshop will focus on both the theoretical and practical aspects of time series data analysis and will provide a platform for researchers and practitioners from academia and industry to discuss potential research directions and critical technical issues and present solutions to tackle related issues in practical applications. We will invite researchers and practitioners from the related areas of AI, machine learning, data science, statistics, and many others to contribute to this workshop.

Although many anomaly detection approaches have been developed for multivariate time series data, limited effort has been made in federated settings in which multivariate time series data are heterogeneously distributed among different edge devices while data sharing is prohibited. In this paper, we investigate the problem of federated unsupervised anomaly detection and present a Federated Exemplar-based Deep Neural Network (Fed-ExDNN) to conduct anomaly detection for multivariate time series data on different edge devices. Specifically, we first design an Exemplar-based Deep Neural network (ExDNN) for learning local time series representations based on their compatibility with an exemplar module which consists of hidden parameters learned to capture varieties of normal patterns on each edge device. Next, a constrained clustering mechanism (FedCC) is employed on the centralized server to align and aggregate the parameters of different local exemplar modules to obtain a unified global exemplar module. Finally, the global exemplar module is deployed together with a shared feature encoder to each edge device, and anomaly detection is conducted by examining the compatibility of testing data to the exemplar module. Fed-ExDNN captures local normal time series patterns with ExDNN and aggregates these patterns by FedCC, and thus can handle the heterogeneous data distributed over different edge devices simultaneously. Thoroughly empirical studies on six public datasets show that ExDNN and Fed-ExDNN can outperform state-of-the-art anomaly detection algorithms and federated learning techniques, respectively.

Memory replay, which stores a subset of representative historical data from previous tasks to replay while learning new tasks, exhibits state-of-the-art performance for various continual learning applications on Euclidean data. While topological information plays a critical role in characterizing graph data, existing memory replay based graph learning techniques only store individual nodes for replay and do not consider their associated edge information. To this end, we propose a sparsified subgraph memory (SSM), which sparsifies the selected computation graphs into fixed size before storing them into the memory. In this way, we can reduce the memory consumption of a computation subgraph from $\mathcal{O}(d^{L})$ to $\mathcal{O}(1)$, and for the first time enable GNNs to utilize the explicit topological information for memory replay. Finally, our empirical studies show that SSM outperforms state-of-the-art approaches by up to 27.8% on four different public datasets. Unlike existing methods which focus on task incremental learning (task-IL) setting, SSM succeeds in the challenging class incremental learning (class-IL) setting in which a model is required to distinguish all learned classes without task indicators, and even achieves comparable performance to joint training which is the performance upper bound for continual learning. Our code is available at https://github.com/QueuQ/SSM.

Time series data are ubiquitous, and is one of the fastest growing and richest types of data. Recent advances in sensing technologies has resulted in a rapid growth in the size and complexity of time series archives. This demands development of new tools and solutions. The goals of this workshop are to: (1) highlight the significant challenges that underpin learning and mining from time series data (e.g. irregular sampling, spatiotemporal structure, uncertainty quantification), (2) discuss recent algorithmic, theoretical, statistical, or systems-based developments for tackling these problems, and (3) exploring new frontiers in time series analysis and their connections with important topics such as knowledge representation, reasoning, control, and business intelligence. In summary, our workshop will focus on both the theoretical and practical aspects of time series data analysis and will provide a platform for researchers and practitioners from both academia and industry to discuss potential research directions, key technical issues, and present solutions to tackle related issues in practical applications. We will invite researchers and practitioners from the related areas of AI, machine learning, data science, statistics, and many others to contribute to this workshop.

In many high-stakes applications of machine learning models, outputting only predictions or providing statistical confidence is usually insufficient to gain trust from end users, who often prefer a transparent reasoning paradigm. Despite the recent encouraging developments on deep networks for sequential data modeling, due to the highly recursive functions, the underlying rationales of their predictions are difficult to explain. Thus, in this paper, we aim to develop a sequence modeling approach that explains its own predictions by breaking input sequences down into evidencing segments (i.e., sub-sequences) in its reasoning. To this end, we build our model upon convolutional neural networks, which, in their vanilla forms, associates local receptive fields with outputs in an obscure manner. To unveil it, we resort to case-based reasoning, and design prototype modules whose units (i.e., prototypes) resemble exemplar segments in the problem domain. Each prediction is obtained by combining the comparisons between the prototypes and the segments of an input. To enhance interpretability, we propose a training objective that delicately adapts the distribution of prototypes to the data distribution in latent spaces, and design an algorithm to map prototypes to human-understandable segments. Through extensive experiments in a variety of domains, we demonstrate that our model can achieve high interpretability generally, together with a competitive accuracy to the state-of-the-art approaches.

Web personalization, e.g., recommendation or relevance search, tailoring a service/product to accommodate specific online users, is becoming increasingly important. Inductive personalization aims to infer the relations between existing entities and unseen new ones, e.g., searching relevant authors for new papers or recommending new items to users. This problem, however, is challenging since most of recent studies focus on transductive problem for existing entities. In addition, despite some inductive learning approaches have been introduced recently, their performance is sub-optimal due to relatively simple and inflexible architectures for aggregating entity’s content. To this end, we propose the inductive contextual personalization (ICP) framework through contextual relation learning. Specifically, we first formulate the pairwise relations between entities with a ranking optimization scheme that employs neural aggregator to fuse entity’s heterogeneous contents. Next, we introduce a node embedding term to capture entity’s contextual relations, as a smoothness constraint over the prior ranking objective. Finally, the gradient descent procedure with adaptive negative sampling is employed to learn the model parameters. The learned model is capable of inferring the relations between existing entities and inductive ones. Thorough experiments demonstrate that ICP outperforms numerous baseline methods for two different applications, i.e., relevant author search and new item recommendation.

Efficient audio scene classification is essential for smart sensing platforms such as robots, medical monitoring, surveillance, or autonomous vehicles. We propose a retrieval-based scene classification architecture that combines recurrent neural networks and attention to compute embeddings for short audio segments. We train our framework using a custom audio loss function that captures both the relevance of audio segments within a scene and that of sound events within a segment. Using experiments on real audio scenes, we show that we can discriminate audio scenes with high accuracy after listening in for less than a second. This preserves 93% of the detection accuracy obtained after hearing the entire scene.

Stock price movement not only depends on the history of individual stock movements, but also complex hidden dynamics associated with other correlated stocks. Despite the substantial effort made to understand the principles of stock price movement, few attempts have been made to predict movement direction based upon a single stock's historical records together with its correlated stocks. Here, we present a multi-task recurrent neural network (RNN) with high-order Markov random fields (MRFs) to predict stock price movement direction. Specifically, we first design a multi-task RNN framework to extract informative features from the raw market data of individual stocks without considering any domain knowledge. Next, we employ binary MRFs with unary features and weighted lower linear envelopes as the higher-order energy function to capture higher-order consistency within the same stock clique (group). We also derive a latent structural SVM algorithm to learn higher-order MRFs in a polynomial number of iterations. Finally, a sub-gradient algorithm is employed to perform end-to-end training of the RNN and high-order MRFs. We conduct thorough empirical studies on three popular Chinese stock market indexes and the proposed method outperforms baseline approaches. To our best knowledge, the proposed technique is the first to investigate intra-clique relationships with higher-order MRFs for stock price movement prediction.

Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the demand to incorporate heterogeneous structural (graph) information consisting of multiple types of nodes and edges, but also due to the need for considering heterogeneous attributes or contents (e.g., text or image) associated with each node. Despite a substantial amount of effort has been made to homogeneous (or heterogeneous) graph embedding, attributed graph embedding as well as graph neural networks, few of them can jointly consider heterogeneous structural (graph) information as well as heterogeneous contents information of each node effectively. In this paper, we propose HetGNN, a heterogeneous graph neural network model, to resolve this issue. Specifically, we first introduce a random walk with restart strategy to sample a fixed size of strongly correlated heterogeneous neighbors for each node and group them based upon node types. Next, we design a neural network architecture with two modules to aggregate feature information of those sampled neighboring nodes. The first module encodes "deep" feature interactions of heterogeneous contents and generates content embedding for each node. The second module aggregates content (attribute) embeddings of different neighboring groups (types) and further combines them by considering the impacts of different groups to obtain the ultimate node embedding. Finally, we leverage a graph context loss and a mini-batch gradient descent procedure to train the model in an end-to-end manner. Extensive experiments on several datasets demonstrate that HetGNN can outperform state-of-the-art baselines in various graph mining tasks, i.e., link prediction, recommendation, node classification & clustering and inductive node classification & clustering.

Co-clustering partitions instances and features simultaneously by leveraging the duality between them and it often yields impressive performance improvement over traditional clustering algorithms. The recent development in learning deep representations has demonstrated the advantage in extracting effective features. However, the research on leveraging deep learning frameworks for co-clustering is limited for two reasons: 1) current deep clustering approaches usually decouple feature learning and cluster assignment as two separate steps, which cannot yield the task-specific feature representation; 2) existing deep clustering approaches cannot learn representations for instances and features simultaneously. In this paper, we propose a deep learning model for co-clustering called DeepCC. DeepCC utilizes the deep autoencoder for dimension reduction, and employs a variant of Gaussian Mixture Model (GMM) to infer the cluster assignments. A mutual information loss is proposed to bridge the training of instances and features. DeepCC jointly optimizes the parameters of the deep autoencoder and the mixture model in an end-to-end fashion on both the instance and the feature spaces, which can help the deep autoencoder escape from local optima and the mixture model circumvent the Expectation-Maximization (EM) algorithm. To the best of our knowledge, DeepCC is the first deep learning model for co-clustering. Experimental results on various dataseis demonstrate the effectiveness of DeepCC.

Multivariate time series data are becoming increasingly common in numerous real world applications, e.g., power plant monitoring, health care, wearable devices, automobile, etc. As a result, multivariate time series retrieval, i.e., given the current multivariate time series segment, how to obtain its relevant time series segments in the historical data (or in the database), attracts significant amount of interest in many fields. Building such a system, however, is challenging since it requires a compact representation of the raw time series which can explicitly encode the temporal dynamics as well as the correlations (interactions) between different pairs of time series (sensors). Furthermore, it requires query efficiency and expects a returned ranking list with high precision on the top. Despite the fact that various approaches have been developed, few of them can jointly resolve these two challenges. To cope with this issue, in this paper we propose a Deep r-th root of Rank Supervised Joint Binary Embedding (Deep r-RSJBE) to perform multivariate time series retrieval. Given a raw multivariate time series segment, we employ Long Short-Term Memory (LSTM) units to encode the temporal dynamics and utilize Convolutional Neural Networks (CNNs) to encode the correlations (interactions) between different pairs of time series (sensors). Subsequently, a joint binary embedding is pursued to incorporate both the temporal dynamics and the correlations. Finally, we develop a novel r-th root ranking loss to optimize the precision at the top of a Hamming distance ranking list. Thoroughly empirical studies based upon three publicly available time series datasets demonstrate the effectiveness and the efficiency of Deep r-RSJBE.

The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure. Most existing network embedding models, with shallow or deep architectures, learn vertex representations from the sampled vertex sequences such that the low-dimensional embeddings preserve the locality property and/or global reconstruction capability. The resultant representations, however, are difficult for model generalization due to the intrinsic sparsity of sampled sequences from the input network. As such, an ideal approach to address the problem is to generate vertex representations by learning a probability density function over the sampled sequences. However, in many cases, such a distribution in a low-dimensional manifold may not always have an analytic form. In this study, we propose to learn the network representations with adversarially regularized autoencoders (NetRA). NetRA learns smoothly regularized vertex representations that well capture the network structure through jointly considering both locality-preserving and global reconstruction constraints. The joint inference is encapsulated in a generative adversarial training process to circumvent the requirement of an explicit prior distribution, and thus obtains better generalization performance. We demonstrate empirically how well key properties of the network structure are captured and the effectiveness of NetRA on a variety of tasks, including network reconstruction, link prediction, and multi-label classification.

Accurately identifying time-invariant operational relationships among different components is critical to autonomic management of complex manufactural systems. In this paper, we collect time series of sensor readings from manufacturing systems, and propose a solution leveraging Sparse Group LASSO to discover structured pairwise nonlinear relationships and quantify them by mathematical formulas. We consider both real-life operational patterns and underlying physical reactions inside the manufactural systems, which leads to a learning formulation for combined periodic and aperiodic system behaviors. An accelerated gradient descent algorithm is developed to efficiently solve the related optimization problem. We estimate sample correlations between proximal time points to improve the accuracy of the discovered relationships and the nonlinear quantitative formulas. The method is evaluated using both synthetic and real-world datasets, which shows superior performance over the state of the art in discovering nonlinear relationships in manufactural systems.

Complex systems are prevalent in many fields such as finance, security and industry. A fundamental problem in system management is to perform diagnosis in case of system failure such that the causal anomalies, i.e., root causes, can be identified for system debugging and repair. Recently, invariant network has proven a powerful tool in characterizing complex system behaviors. In an invariant network, a node represents a system component, and an edge indicates a stable interaction between two components. Recent approaches have shown that by modeling fault propagation in the invariant network, causal anomalies can be effectively discovered. Despite their success, the existing methods have a major limitation: they typically assume there is only a single and global fault propagation in the entire network. However, in real-world large-scale complex systems, it's more common for multiple fault propagations to grow simultaneously and locally within different node clusters and jointly define the system failure status. Inspired by this key observation, we propose a two-phase framework to identify and rank causal anomalies. In the first phase, a probabilistic clustering is performed to uncover impaired node clusters in the invariant network. Then, in the second phase, a low-rank network diffusion model is designed to backtrack causal anomalies in different impaired clusters. Extensive experimental results on real-life datasets demonstrate the effectiveness of our method.

In recent years, hashing techniques are becoming overwhelmingly popular for their high efficiency in handling large-scale computer vision applications. It has been shown that hashing techniques which leverage supervised information can significantly enhance performance, and thus greatly benefit visual search tasks. Typically, a modern hashing method uses a set of hash functions to compress data samples into compact binary codes. However, few methods have developed hash functions to optimize the precision at the top of a ranking list based upon Hamming distances. In this paper, we propose a novel supervised hashing approach, namely Rank Preserving Hashing (RPH), to explicitly optimize the precision of Hamming distance ranking towards preserving the supervised rank information. The core idea is to train disciplined hash functions in which the mistakes at the top of a Hamming-distance ranking list are penalized more than those at the bottom. To find such hash functions, we relax the original discrete optimization objective to a continuous surrogate, and then design an online learning algorithm to efficiently optimize the surrogate objective. Empirical studies based upon two benchmark image datasets demonstrate that the proposed hashing approach achieves superior image search accuracy over the state-of-the-art approaches.

Inferring potential links is a fundamental problem in social networks. In the link recommendation problem, the aim is to suggest a list of potential people to each user, ordered by the preferences of the user. Although various approaches have been developed to solve this problem, the difficulty of producing a ranking list with high precision at the top -- the most important consideration for real world applications -- remains largely an open problem. In this work, we propose two top-k link recommendation algorithms which focus on optimizing the top ranked links. For this purpose, we define a cost-sensitive ranking loss which penalizes the mistakes at the top of a ranked list more than the mistakes at the bottom. In particular, we propose a log loss, derive its surrogate, and formulate a top-k link recommendation model by optimizing this surrogate loss function based upon latent features. Moreover, we extend this top-k link recommendation model by incorporating both the latent features and explicit features of the network. Finally, an efficient learning scheme to learn the model parameters is provided. We conduct empirical studies based upon four real world datasets, i.e., Wikipedia, CondMat, Epinions, and MovieLens 1M, of which the largest network contains more than 70 thousand nodes and over one million links. Our experiments demonstrate that the proposed algorithms outperform several state-of-the-art methods.

Signed networks, in which the relationship between two nodes can be either positive (indicating a relationship such as trust) or negative (indicating a relationship such as distrust), are becoming increasingly common. A plausible model for user behavior analytics in signed networks can be based upon the assumption that more extreme positive and negative relationships are explored and exploited before less extreme ones. Such a model implies that a personalized ranking list of latent links should place positive links on the top, negative links at the bottom, and unknown status links in between. Traditional ranking metrics, e.g., area under the receiver operating characteristic curve (AUC), are however not suitable for quantifying such a ranking list which includes positive, negative, and unknown status links. To address this issue, a generalized AUC (GAUC) which can measure both the head and tail of a ranking list has been introduced. Since GAUC weights each pairwise comparison equally and the calculation of GAUC requires quadratic time, we derive two lower bounds of GAUC which can be computed in linear time and put more emphasis on ranking positive links on the top and negative links at the bottom of a ranking list. Next, we develop two efficient latent link recommendation (ELLR) algorithms in order to recommend links by directly optimizing these two lower bounds, respectively. Finally, we compare these two ELLR algorithms with top-performing baseline methods over four benchmark datasets, among which the largest network has more than 100 thousand nodes and seven million entries. Thorough empirical studies demonstrate that the proposed ELLR algorithms outperform state-of-the-art approaches for link recommendation in signed networks at no cost in efficiency.

Signed directed social networks, in which the relationships between users can be either positive (indicating relations such as trust) or negative (indicating relations such as distrust), are increasingly common. Thus the interplay between positive and negative relationships in such networks has become an important research topic. Most recent investigations focus upon edge sign inference using structural balance theory or social status theory. Neither of these two theories, however, can explain an observed edge sign well when the two nodes connected by this edge do not share a common neighbor (e.g., common friend). In this paper we develop a novel approach to handle this situation by applying a new model for node types. Initially, we analyze the local node structure in a fully observed signed directed network, inferring underlying node types. The sign of an edge between two nodes must be consistent with their types; this explains edge signs well even when there are no common neighbors. We show, moreover, that our approach can be extended to incorporate directed triads, when they exist, just as in models based upon structural balance or social status theory. We compute Bayesian node types within empirical studies based upon partially observed Wikipedia, Slashdot, and Epinions networks in which the largest network (Epinions) has 119K nodes and 841K edges. Our approach yields better performance than state-of-the-art approaches for these three signed directed networks.