The inherent connectivity and dependency of graph-structured data, combined with its unique topology-driven access patterns, pose fundamental challenges to conventional data replication and request routing strategies in geo-distributed cloud storage systems. In this paper, we propose GeoLayer, a geo-distributed graph storage framework that jointly optimizes graph replica placement and pattern request routing. We first construct a latency-aware layered graph architecture that decomposes the graph topology into multiple layers, aiming to reduce the decision space and computational complexity of the optimization problem, while mitigating the impact of network heterogeneity in geo-distributed environments. Building on the layered graph, we introduce an overlap-centric replica placement scheme to accommodate the diversity of graph pattern accesses, along with a directed heat diffusion model that captures heat conduction and superposition effects to guide data allocation. For request routing, we develop a stepwise layered routing strategy that performs progressive expansion over the layered graph to efficiently retrieve the required data. Experimental results show that, compared to state-of-the-art replica placement and routing schemes, GeoLayer achieves a 1.34x - 3.67x improvement in response times for online graph pattern requests and a 1.28x - 3.56x speedup in offline graph analysis performance.

Various graph models have emerged to meet diverse application needs, each with unique characteristics and specialties. Managing and analyzing graph data inevitably requires interactions across different models to serve upstream business requirements. Therefore, an Extract-Transform-Load (ETL) tool designed to bridge different graph models is desired. In this paper, we propose GETL, a generalized graph ETL framework capable of automatically identifying graph model schemas and performing seamless data conversion among RDF, RDF-star, labeled property graph, and the relational model. This is attributed to GETL's unified graph representation model, constructed as nested label, entity pairs, offering powerful capabilities in graph representation and model compatibility. Additionally, we develop a unified programming interface to support complex graph transformation tasks. It is built upon the Gremlin syntax and provides strong expressive capabilities. Finally, our evaluation demonstrates that GETL outperforms state-of-the-art solutions in terms of model conversion efficiency and data manipulation language (DML) intelligibility.

We focus on the problem of event sequence clustering with different temporal patterns from the view of Reinforcement Learning (RL), whereby the observed sequences are assumed to be generated from a mixture of latent policies. We propose an Expectation-Maximization (EM) based algorithm to cluster the sequences with different temporal patterns into the underlying policies while simultaneously learning each of the policy model, in E-step estimating the cluster labels for each sequence, in M-step learning the respective policy. For each policy learning, we resort to Inverse Reinforcement Learning (IRL) by decomposing the observed sequence into states (hidden embedding of event history) and actions (time interval to next event) in order to learn a reward function. Experiments on synthetic and real-world datasets show the efficacy of our method against the state-of-the-arts.

Anomalies are ubiquitous in real-world time-series data which call for effective and timely detection, especially in an unsupervised setting for labeling cost saving. In this paper, we develop an unsupervised density reconstruction model for multi-dimensional time-series anomaly detection. In particular, it directly handles an important realistic setting that the detection is achieved towards raw time-series contaminated with noise for training, in contrast to most existing anomaly detection works that assume the training data is in general clean i.e. not contaminated with anomaly. It extends recent advancements in deep generative models and state space models to achieve robust anomaly detection. Our approach comprises of a novel state space based generative model, a filtering based inference model, together with a carefully-designated emission model based on robust statistics theory. Extensive experimental results are conducted to show that our approach can adapt to complex patterns even given severely contaminated training data. We also develop visualization techniques to help better understand the behavior of the anomaly detection models. Empirical results show that our method outperforms state-of-the-arts on both synthetic and real-world datasets.

Time-series and event sequences are widely collected data types in real-world applications. Modeling and forecasting of such temporal data play an important role in an informed decision-making process. A major limitation of previous methods is that they either focus on time-series or events, rather than the combination of the two worlds. In fact, the two types of data often provide complementary information, emphasizing the necessity of jointly modeling the both. In this paper, we propose the RNN-ODE collaborative model for joint modeling and forecasting of heterogeneous time-series and event sequence data, which combines several useful techniques from both Bayesian and deep learning for its interpretability. Specifically, we devise a tailored encoder to combine the advances in deep temporal point processes models and variational recurrent neural networks. To predict the probability of event occurrence over an arbitrary continuous-time horizon, we base our model on the mathematical foundation of Neural Ordinary Differential Equations (NODE). Extensive experimental results on simulations and real data sets show that compared with existing methods, our integrated approach can achieve more competitive forecasting performance of both time-series and event sequences.

Negative pairs, especially hard negatives as combined with common negatives (easy to discriminate), are essential in contrastive learning, which plays a role of avoiding degenerate solutions in the sense of constant representation across different instances. Inspired by recent hard negative mining methods via pairwise mixup operation in vision, we propose M-Mix, which dynamically generates a sequence of hard negatives. Compared with previous methods, M-Mix mainly has three features: 1) adaptively choose samples to mix; 2) simultaneously mix multiple samples; 3) automatically assign different mixing weights to the selected samples. We evaluate our method on two image datasets (CIFAR-10, CIFAR-100), five node classification datasets (PPI, DBLP, Pubmed, etc), five graph classification datasets (IMDB, PTC_MR, etc), and two downstream combinatorial tasks (graph edit distance and node clustering). Results show that it achieves state-of-the-art performance under self-supervised settings. Code is available at: https://github.com/Sherrylone/m-mix.

Brain-inspired hyperdimensional computing (HDC) has been introduced as an alternative computing paradigm to achieve efficient and robust learning. HDC simulates cognitive tasks by mapping all data points to patterns of neural activity in the high-dimensional space, which has demonstrated promising performances in a wide range of applications such as robotics, biomedical signal processing, and genome sequencing. Language tasks, generally solved using machine learning methods, are widely deployed on low-power embedded devices. However, existing HDC solutions suffer from major challenges that impede the deployment of low-power embedded devices: the storage and computation overhead of HDC models grows dramatically with (i) the number of dimensions and (ii) the complex similarity metric during the inference.

Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms – including both shallow and deep ones – often model such dynamics independently, i.e., user static and dynamic preferences are not modeled under the same latent space, which makes it difficult to fuse them for recommendation. This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences, namelytranslating sequence to preference. To this end, we formulate the sequential recommendation task as a dictionary learning problem, which learns: 1) a shareddictionary matrix, each row of which represents a partial signal of user dynamic preferences shared across users; and 2) aposterior distribution estimatorusing a deep autoregressive model integrated with Gated Recurrent Unit (GRU), which can select related rows of the dictionary to represent a user's dynamic preferences conditioned on his/her past behaviors. Qualitative studies on the Netflix dataset demonstrate that the proposed method can capture the user preference drifts over time and quantitative studies on multiple real-world datasets demonstrate that the proposed method can achieve higher accuracy compared with state-of-the-art factorization and neural sequential recommendation methods.

Temporal point process (TPP) is an expressive tool for modeling the temporal pattern of event sequences. However, discovering temporal patterns for event sequences clustering is rarely studied in TPP modeling. To solve this problem, we take a reinforcement learning view whereby the observed sequences are assumed to be generated from a mixture of latent policies. The purpose is to cluster the sequences with different temporal patterns into the underlying policies while learning each of the policy model. The flexibility of our model lies in: i) all the components are networks including the policy network for modeling the temporal point process; ii) to handle varying-length event sequences, we resort to inverse reinforcement learning by decomposing the observed sequence into states (RNN hidden embedding of history) and actions (time interval to next event) in order to learn a reward function, it helps to achieve better performance or increasing efficiency compared to existing methods using rewards over the entire sequence such as log-likelihood or Wasserstein distance. We adopt an Expectation-Maximization algorithm, in E-step estimating the cluster labels for each sequence, in M-step aiming to learn the respective policy. Extensive experiments on synthetic and real-world datasets show the efficacy of our method against the state-of-the-arts.

Ensemble matrix approximation (MA) methods have achieved promising performance in collaborative filtering, many of which perform matrix approximation on multiple submatrices of user-item ratings in parallel and then combine the predictions from the sub-models for higher efficiency. However, data partitioning could lead to suboptimal accuracy due to the lack of capturing structural information related to most or all users/items. This paper proposes a new ensemble learning framework, in which the local models and global models are synergetically updated from each other. This makes it possible to capture both local associations in user-item subgroups and global structures over all users and items. Experiments on three real-world datasets demonstrate that the proposed method outperforms six state-of-the-art methods in recommendation accuracy with decent scalability.

This paper presents a factorial marked temporal point process model and presents efficient learning methods. In conventional (multi-dimensional) marked temporal point process models, an event is often encoded by a single discrete variable (marker). We describe the factorial marked point processes whereby time-stamped event is factored into multiple markers. Accordingly the size of the infectivity matrix modeling the effect between pairwise markers is in exponential order regarding the number of discrete markers. We propose a decoupled learning method with two learning procedures: i) directly solving the model based on two techniques: Alternating Direction Method of Multipliers and Fast Iterative Shrinkage-Thresholding Algorithm; ii) involving a reformulation that transforms the original problem into a Logistic Regression model for more efficient learning. Moreover, a sparse group regularizer is added to identify the key profile features and event labels. Empirical results on real world datasets demonstrate the efficiency of our decoupled and reformulated method.

In this work, we explore the learning task of mixtures of Markov chains (MMCs) from aggregate data. Our work demonstrates that although this challenging task is generally intractable because of the identifiability problem, it can be solved approximately by imposing structural constraints on its transition matrices Specifically, the proposed structural constraints include specifying active state sets corresponding to the chains and adding a series of pairwise sparse regularizers on transition matrices. Based on these two structural constraints, we propose a constrained least-squares method to learn mixtures of Markov chains. We develop a novel iterative algorithm that decomposes the overall problem into a set of convex subproblems and solves each subproblem efficiently. Experimental results on synthetic data prove that our learning method converges well and is robust to the noise in data. Moreover, the comparison with state-of-art competitors on real-world data further validates the superiority of our method.

Graph matching, which refers to a class of computational problems of finding an optimal correspondence between the vertices of graphs to minimize (maximize) their node and edge disagreements (affinities), is a fundamental problem in computer science and relates to many areas such as combinatorics, pattern recognition, multimedia and computer vision. Compared with the exact graph (sub)isomorphism often considered in a theoretical setting, inexact weighted graph matching receives more attentions due to its flexibility and practical utility. A short review of the recent research activity concerning (inexact) weighted graph matching is presented, detailing the methodologies, formulations, and algorithms. It highlights the methods under several key bullets, e.g. how many graphs are involved, how the affinity is modeled, how the problem order is explored, and how the matching procedure is conducted etc. Moreover, the research activity at the forefront of graph matching applications especially in computer vision, multimedia and machine learning is reported. The aim is to provide a systematic and compact framework regarding the recent development and the current state-of-the-arts in graph matching.

Statistical models based on Markov chains, especially mixtures of Markov chains, have recently been studied and demonstrated to be effective in various data mining applications such as tourist flow analysis, animal migration modeling, and transportation administration. Nevertheless, the research so far has mainly focused on analyzing data at individual levels. Due to security and privacy reasons, however, the observations in practice usually consist of coarse-grained statistics of individual data,a.k.a.aggregate data, rendering learning mixtures of Markov chains an even more challenging problem. In this work, we show that this challenging problem, although intractable in its original form, can be solved approximately by posing structural constraints on the transition matrices. The proposed structural constraints include specifying active state sets corresponding to the chains and adding a pairwise sparse regularization term on transition matrices. Based on these two structural constraints, we propose a constrained least-squares method to learn mixtures of Markov chains. We further develop a novel iterative algorithm that decomposes the overall problem into a set of convex subproblems and solves each subproblem efficiently, making it possible to effectively learn mixtures of Markov chains from aggregate data. We propose a framework for generating synthetic data and analyze the complexity of our algorithm. Additionally, the empirical results of the convergence and the robustness of our algorithm are also presented. These results demonstrate the effectiveness and efficiency of the proposed algorithm, comparing with traditional methods. Experimental results on real-world data sets further validate that our algorithm can be used to solve practical problems.

This paper researches on Matthew Effect in Sina Weibo microblogger. We choose the microblogs in the ranking list of Hot Microblog App in Sina Weibo microblogger as target of our study. The differences of repost number of microblogs in the ranking list between before and after the time when it enter the ranking list of Hot Microblog app are analyzed. And we compare the spread features of the microblogs in the ranking list with those hot microblogs not in the list and those ordinary microblogs of users who have some microblog in the ranking list before. Our study proves the existence of Matthew Effect in social network.

Twitter displays the tweets a user received in a reversed chronological order, which is not always the best choice. As Twitter is full of messages of very different qualities, many informative or relevant tweets might be flooded or displayed at the bottom while some nonsense buzzes might be ranked higher. In this work, we present a supervised learning method for personalized tweets reordering based on user interests. User activities on Twitter, in terms of tweeting, retweeting, and replying, are leveraged to obtain the training data for reordering models. Through exploring a rich set of social and personalized features, we model the relevance of tweets by minimizing the pairwise loss of relevant and irrelevant tweets. The tweets are then reordered according to the predicted relevance scores. Experimental results with real twitter user activities demonstrated the effectiveness of our method. The new method achieved above 30% accuracy gain compared with the default ordering in twitter based on time.

For a broad-topic and ambiguous query, different users may have different search goals when they submit it to a search engine. The inference and analysis of user search goals can be very useful in improving search engine relevance and user experience. In this paper, we propose a novel approach to infer user search goals by analyzing search engine query logs. First, we propose a framework to discover different user search goals for a query by clustering the proposed feedback sessions. Feedback sessions are constructed from user click-through logs and can efficiently reflect the information needs of users. Second, we propose a novel approach to generate pseudo-documents to better represent the feedback sessions for clustering. Finally, we propose a new criterion )“Classified Average Precision (CAP)” to evaluate the performance of inferring user search goals. Experimental results are presented using user click-through logs from a commercial search engine to validate the effectiveness of our proposed methods.

In this poster we report our study on the microblog spammers with samples attracted by 50 honeyspots from two popular Chinese microblogging networks: Sina Weibo (weibo.com), and Ten cent Weibo (t.QQ.com) in seven months. We studied their features such as social information, activity, account age and spamming strategy. Several distinguishing characteristics of spammers on these two social network communities are observed, which can be helpful to the further study on automatic detection of microblog spammers. To our best knowledge our work is the first of its kind on the analysis of features of Chinese micloblog spammers.

As the popularity of social networking sites increase, so does their attractiveness for criminals. In this work, we show how an adversary can build artificial identities using semantic information in social network. Our method make the identities look more like real people, therefore can be used to support many kinds of attacks, such as ASE, profile cloning. A prototype of this method is implemented, includes following stages: Firstly, categories of virtual identity are predefined, and each category has multiple properties, such as geographical region, hobby, education, age, interested topic/keywords, etc. Secondly, based on category information, each identity will foster its own "life" semantically, such as edit profile and update status, find hot related news/topic from Google then post to wall, find related groups/networks then request to add in, and find/like/create/comment pages/posts, etc. Thirdly, artificial identity will evolve to multiple stages according to its status (for example, number of friends of real people), single identity with different evolutionary stages is linked together to a group that will help to ensure the number of attack edges.

Wyner-Ziv coding is presented in this paper. It is extended to the scenario of multivariate source and side information, whose rate-distortion function is obtained by a reverse water-filling method for the joint quadratic-Gaussian case.