Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many domains, it is highly desirable to derive data-driven global explanations or rules that can better explain the high-level properties of the models and data in question. However, evaluating global counterfactual explanations is hard in real-world datasets due to a lack of human-annotated ground truth, which limits their use in areas like molecular sciences. Additionally, the increasing scale of these datasets provides a challenge for random search-based methods. In this paper, we develop a novel global explanation model RLHEX for molecular property prediction. It aligns the counterfactual explanations with human-defined principles, making the explanations more interpretable and easy for experts to evaluate. RLHEX includes a VAE-based graph generator to generate global explanations and an adapter to adjust the latent representation space to human-defined principles. Optimized by Proximal Policy Optimization (PPO), the global explanations produced by RLHEX cover 4.12% more input graphs and reduce the distance between the counterfactual explanation set and the input set by 0.47% on average across three molecular datasets. RLHEX provides a flexible framework to incorporate different human-designed principles into the counterfactual explanation generation process, aligning these explanations with domain expertise. The code and data are released at https://github.com/dqwang122/RLHEX.

Antimicrobial peptides (AMPs) are promising therapeutic approaches against drug-resistant pathogens. Recently, deep generative models are used to discover new AMPs. However, previous studies mainly focus on peptide sequence attributes and do not consider crucial structure information. In this paper, we propose a latent sequence-structure model for designing AMPs (LSSAMP). LSSAMP exploits multi-scale vector quantization in the latent space to represent secondary structures (e.g. alpha helix and beta sheet). By sampling in the latent space, LSSAMP can simultaneously generate peptides with ideal sequence attributes and secondary structures. Experimental results show that the peptides generated by LSSAMP have a high probability of antimicrobial activity. Our wet laboratory experiments verified that two of the 21 candidates exhibit strong antimicrobial activity. The code is released at https://github.com/dqwang122/LSSAMP.

Preference diversity arouses much research attention in recent years, as it is believed to be closely related to many profound problems such as user activeness in social media or recommendation systems. However, due to the lack of large-scale data with comprehensive user behavior log and accurate content labels, the real quantitative effect of preference diversity on user activeness is still largely unknown. This paper studies the heterogeneous effect of preference diversity on user activeness in social media. We examine large-scale real-world datasets collected from two of the most popular video-sharing social platforms in China, including the behavior logs of more than 787 thousand users and 1.95 million videos, with accurate content category information. We investigate the distribution and evolution of preference diversity, and find rich heterogeneity in the effect of preference diversity on the dynamic activeness. Furthermore, we discover the divergence of preference diversity mechanisms for the same user under different usage scenarios, such as active (where users actively seek information) and passive (where users passively receive information) modes. Unlike existing qualitative studies, we propose a universal mixture model with the capability of accurately fitting dynamic activeness curves while reflecting the heterogeneous patterns of preference diversity. To our best knowledge, this is the first quantitative model that incorporates the effect of preference diversity on user activeness. With the modeling parameters, we are able to make accurate churn and activeness predictions and provide decision support for increasing user activity through the intervention of diversity. Our findings and model comprehensively reveal the significance of preference diversity and provide potential implications for the design of future recommendation systems and social media.

Sponsored search auction is a crucial component of modern search engines. It requires a set of candidate bidwords that advertisers can place bids on. Existing methods generate bidwords from search queries or advertisement content. However, they suffer from the data noise in (query, bidword) and (advertisement, bidword) pairs. In this paper, we propose a triangular bidword generation model (TRIDENT), which takes the high-quality data of paired (query, advertisement) as a supervision signal to indirectly guide the bidword generation process. Our proposed model is simple yet effective: by using bidword as the bridge between search query and advertisement, the generation of search query, advertisement and bidword can be jointly learned in the triangular training framework. This alleviates the problem that the training data of bidword may be noisy. Experimental results, including automatic and human evaluations, show that our proposed TRIDENT can generate relevant and diverse bidwords for both search queries and advertisements. Our evaluation on online real data validates the effectiveness of the TRIDENT's generated bidwords for product search.

In this paper, we present the design of QuAChIE, a Question Answering based Chinese Information Extraction system. QuAChIE mainly depends on a well-trained question answering model to extract high-quality triples. The group of head entity and relation are regarded as a question given the input text as the context. For the training and evaluation of each model in the system, we build a large-scale information extraction dataset using Wikidata and Wikipedia pages by distant supervision. The advanced models implemented on top of the pre-trained language model and the enormous distant supervision data enable QuAChIE to extract relation triples from documents with cross-sentence correlations. The experimental results on the test set and the case study based on the interactive demonstration show its satisfactory Information Extraction quality on Chinese document-level texts.

Modern online auction-based advertising systems combine item and user features to promote ad creatives with the most revenue.However, new ad creatives have to display for certain initial users before enough click statistics could collected and utilized in later ads ranking and bidding processes. This leads to a well-known challenging cold start problem.In this paper, we argue that the content of the creatives intrinsically determines their performance (e.g. ctr, cvr), and we add a pre-ranking stage based on the content. The stage prunes inferior creatives and thus makes online impressions more effective. Since the pre-ranking stage can be executed offline, we can use deep features and take their well generalization to navigate the cold start problem.Specifically, we propose Pre Evaluation Ad Creation Model (PEAC), a novel method to evaluate creatives even before they were shown in the online ads system. Our proposed PEAC only utilizes ads information such as verbal and visual content, but requires no user data as features. During the online A/B testing, PEAC shows significant improvement in revenue. The method has been implemented and deployed in the large scale online advertising system at ByteDance. Furthermore, we provide detailed analysis on what the model learns, which also gives suggestions for ad creative design.

Recent years witness the merge of social networks and user-generated content (UGC) platforms. In these new platforms, users establish links to others not only driven by their social relationships in the physical world but also driven by the contents published by others. During this merging process, social networks gradually integrate both social and content links and become unprecedentedly complicated, with the motivation to exploit both the advantages of social viscosity and content attractiveness to reach the best customer retention situation. However, due to the lack of fine-grained data recording such merging phenomena, the co-driven mechanism of social and content links in churn remains unexplored. How do social and content factors jointly influence customers' churn? What is the best ratio of social and content links for retention? Is there a model to capture this co-driven mechanism in churn phenomena? In this paper, we collect a real-world dataset with more than 5.77 million users and 1.15 billion links, with each link being tagged as a social one or a content one. We find that both social and content links have a significant impact on users' churn and they work jointly as a complicated mixture effect. As a result, we propose a novel survival model, which incorporates both social and content factors, to predict churn probability over time. Our model successfully fits the churn distribution in reality and accurately predicts the churn rate of different subpopulations in the future. By analyzing the modeling parameters, we try to strike a balance between social-driven and content-driven links in a user's social network to reach the lowest churn rate. Our model and findings may have potential implications for the design of future social media.

The recent explosion in the adoption of search engines and new media such as blogs and Twitter have facilitated the faster propagation of news and rumors. How quickly does a piece of news spread over these media? How does its popularity diminish over time? Does the rising and falling pattern follow a simple universal law? In this article, we propose SpikeM, a concise yet flexible analytical model of the rise and fall patterns of information diffusion. Our model has the following advantages. First, unification power: it explains earlier empirical observations and generalizes theoretical models including the SI and SIR models. We provide the threshold of the take-off versus die-out conditions for SpikeM and discuss the generality of our model by applying it to an arbitrary graph topology. Second, practicality: it matches the observed behavior of diverse sets of real data. Third, parsimony: it requires only a handful of parameters. Fourth, usefulness: it makes it possible to perform analytic tasks such as forecasting, spotting anomalies, and interpretation by reverse engineering the system parameters of interest (quality of news, number of interested bloggers, etc.). We also introduce an efficient and effective algorithm for the real-time monitoring of information diffusion, namely SpikeStream, which identifies multiple diffusion patterns in a large collection of online event streams. Extensive experiments on real datasets demonstrate that SpikeM accurately and succinctly describes all patterns of the rise and fall spikes in social networks.

Do mobile phone calls at larger granularities behave in the same pattern as in smaller ones? How can we forecast the distribution of a whole month's phone calls with only one day's observation? There are many models developed to interpret large scale social graphs. However, all of the existing models focus on graph at one time scale. Many dynamical behaviors were either ignored, or handled at one scale. In particular new users might join or current users quit social networks at any time. In this paper, we propose HiP, a novel model to capture longitudinal behaviors in modeling degree distribution of evolving social graphs. We analyze a large scale phone call dataset using HiP, and compare with several previous models in literature. Our model is able to fit phone call distribution at multiple scales with 30% to 75% improvement over the best existing method on each scale.

User review is a crucial component of open mobile app markets such as the Google Play Store. How do we automatically summarize millions of user reviews and make sense out of them? Unfortunately, beyond simple summaries such as histograms of user ratings, there are few analytic tools that can provide insights into user reviews. In this paper, we propose Wiscom, a system that can analyze tens of millions user ratings and comments in mobile app markets at three different levels of detail. Our system is able to (a) discover inconsistencies in reviews; (b) identify reasons why users like or dislike a given app, and provide an interactive, zoomable view of how users' reviews evolve over time; and (c) provide valuable insights into the entire app market, identifying users' major concerns and preferences of different types of apps. Results using our techniques are reported on a 32GB dataset consisting of over 13 million user reviews of 171,493 Android apps in the Google Play Store. We discuss how the techniques presented herein can be deployed to help a mobile app market operator such as Google as well as individual app developers and end-users.

Given a network, intuitively two nodes belong to the same role if they have similar structural behavior. Roles should be automatically determined from the data, and could be, for example, "clique-members," "periphery-nodes," etc. Roles enable numerous novel and useful network-mining tasks, such as sense-making, searching for similar nodes, and node classification. This paper addresses the question: Given a graph, how can we automatically discover roles for nodes? We propose RolX (Role eXtraction), a scalable (linear in the number of edges), unsupervised learning approach for automatically extracting structural roles from general network data. We demonstrate the effectiveness of RolX on several network-mining tasks: from exploratory data analysis to network transfer learning. Moreover, we compare network role discovery with network community discovery. We highlight fundamental differences between the two (e.g., roles generalize across disconnected networks, communities do not); and show that the two approaches are complimentary in nature.

The recent explosion in the adoption of search engines and new media such as blogs and Twitter have facilitated faster propagation of news and rumors. How quickly does a piece of news spread over these media? How does its popularity diminish over time? Does the rising and falling pattern follow a simple universal law?

Given a graph, how can we extract good features for the nodes? For example, given two large graphs from the same domain, how can we use information in one to do classification in the other (i.e., perform across-network classification or transfer learning on graphs)? Also, if one of the graphs is anonymized, how can we use information in one to de-anonymize the other? The key step in all such graph mining tasks is to find effective node features. We propose ReFeX (Recursive Feature eXtraction), a novel algorithm, that recursively combines local (node-based) features with neighborhood (egonet-based) features; and outputs regional features -- capturing "behavioral" information. We demonstrate how these powerful regional features can be used in within-network and across-network classification and de-anonymization tasks -- without relying on homophily, or the availability of class labels. The contributions of our work are as follows: (a) ReFeX is scalable and (b) it is effective, capturing regional ("behavioral") information in large graphs. We report experiments on real graphs from various domains with over 1M edges, where ReFeX outperforms its competitors on typical graph mining tasks like network classification and de-anonymization.

Efficient thermal management is important in modern data centers as cooling consumes up to 50% of the total energy. Unlike previous work, we consider proactive thermal management, whereby servers can predict potential overheating events due to dynamics in data center configuration and workload, giving operators enough time to react. However, such forecasting is very challenging due to data center scales and complexity. Moreover, such a physical system is influenced by cyber effects, including workload scheduling in servers. We propose ThermoCast, a novel thermal forecasting model to predict the temperatures surrounding the servers in a data center, based on continuous streams of temperature and airflow measurements. Our approach is (a) capable of capturing cyberphysical interactions and automatically learning them from data; (b) computationally and physically scalable to data center scales; (c) able to provide online prediction with real-time sensor measurements. The paper's main contributions are: (i) We provide a systematic approach to integrate physical laws and sensor observations in a data center; (ii) We provide an algorithm that uses sensor data to learn the parameters of a data center's cyber-physical system. In turn, this ability enables us to reduce model complexity compared to full-fledged fluid dynamics models, while maintaining forecast accuracy; (iii) Unlike previous simulation-based studies, we perform experiments in a production data center. Using real data traces, we show that ThermoCast forecasts temperature better than a machine learning approach solely driven by data, and can successfully predict thermal alarms 4.2 minutes ahead of time.

Given a large stream of users clicking on web sites, how can we find trends, patterns and anomalies? We have developed a novel method, WindMine, and its fine-tuning sibling, WindMine-part, to find patterns and anomalies in such datasets. Our approach has the following advantages: (a) it is effective in discovering meaningful “building blocks” and patterns such as the lunch-break trend and anomalies, (b) it automatically determines suitable window sizes, and (c) it is fast, with its wall clock time linear on the duration of sequences. Moreover, it can be made sub-quadratic on the number of sequences (WindMine-part), with little loss of accuracy. We examine the effectiveness and scalability by performing experiments on 67 GB of real data (one billion clicks for 30 days). Our proposed WindMine does produce concise, informative and interesting patterns. We also show that WindMine-part can be easily implemented in a parallel or distributed setting, and that, even in a single-machine setting, it can be an order of magnitude faster (up to 70 times) than the plain version.

Advances in data collection and storage capacity have made it increasingly possible to collect highly volatile graph data for analysis. Existing graph analysis techniques are not appropriate for such data, especially in cases where streaming or near-real-time results are required. An example that has drawn significant research interest is the cyber-security domain, where internet communication traces are collected and real-time discovery of events, behaviors, patterns, and anomalies is desired. We propose MetricForensics, a scalable framework for analysis of volatile graphs. MetricForensics combines a multi-level "drill down" approach, a collection of user-selected graph metrics, and a collection of analysis techniques. At each successive level, more sophisticated metrics are computed and the graph is viewed at finer temporal resolutions. In this way, MetricForensics scales to highly volatile graphs by only allocating resources for computationally expensive analysis when an interesting event is discovered at a coarser resolution first. We test MetricForensics on three real-world graphs: an enterprise IP trace, a trace of legitimate and malicious network traffic from a research institution, and the MIT Reality Mining proximity sensor data. Our largest graph has 3M vertices and 32M edges, spanning 4.5 days. The results demonstrate the scalability and capability of MetricForensics in analyzing volatile graphs; and highlight four novel phenomena in such graphs: elbows, broken correlations, prolonged spikes, and lightweight stars.

We study the problem of mining and summarizing multiple time series effectively and efficiently. We propose PLiF, a novel method to discover essential characteristics ("fingerprints"), by exploiting the joint dynamics in numerical sequences. Our fingerprinting method has the following benefits: (a) it leads to interpretable features; (b) it is versatile: PLiF enables numerous mining tasks, including clustering, compression, visualization, forecasting, and segmentation, matching top competitors in each task; and (c) it is fast and scalable , with linear complexity on the length of the sequences. We did experiments on both synthetic and real datasets, including human motion capture data (17MB of human motions), sensor data (166 sensors), and network router traffic data (18 million raw updates over 2 years). Despite its generality, PLiF outperforms the top clustering methods on clustering; the top compression methods on compression (3 times better reconstruction error, for the same compression ratio); it gives meaningful visualization and at the same time, enjoys a linear scale-up.

Given multiple time sequences with missing values, we propose DynaMMo which summarizes, compresses, and finds latent variables. The idea is to discover hidden variables and learn their dynamics, making our algorithm able to function even when there are missing values.We performed experiments on both real and synthetic datasets spanning several megabytes, including motion capture sequences and chlorine levels in drinking water. We show that our proposed DynaMMo method (a) can successfully learn the latent variables and their evolution; (b) can provide high compression for little loss of reconstruction accuracy; (c) can extract compact but powerful features for segmentation, interpretation, and forecasting; (d) has complexity linear on the duration of sequences.

Multi-core processors with ever increasing number of cores per chip are becoming prevalent in modern parallel computing. Our goal is to make use of the multi-core as well as multi-processor architectures to speed up data mining algorithms. Specifically, we present a parallel algorithm for approximate learning of Linear Dynamical Systems (LDS), also known as Kalman Filters (KF). LDSs are widely used in time series analysis such as motion capture modeling, visual tracking etc. We propose Cut-And-Stitch (CAS), a novel method to handle the data dependencies from the chain structure of hidden variables in LDS, so as to parallelize the EM-based parameter learning algorithm. We implement the algorithm using OpenMP on both a supercomputer and a quad-core commercial desktop. The experimental results show that parallel algorithms using Cut-And-Stitch achieve comparable accuracy and almost linear speedups over the serial version. In addition, Cut-And-Stitch can be generalized to other models with similar linear structures such as Hidden Markov Models (HMM) and Switching Kalman Filters (SKF).

We define and solve the problem of “distribution classification”, and, in general, “distribution mining”. Given n distributions (i.e., clouds) of multi-dimensional points, we want to classify them into k classes, to find patterns, rules and out-lier clouds. For example, consider the 2-d case of sales of items, where, for each item sold, we record the unit price and quantity; then, each customer is represented as a distribution/cloud of 2-d points (one for each item he bought). We want to group similar users together, e.g., for market segmentation, anomaly/fraud detection. We propose D-Mine to achieve this goal. Our main contribution is Theorem 3.1, which shows how to use wavelets to speed up the cloud-similarity computations. Extensive experiments on both synthetic and real multi-dimensional data sets show that our method achieves up to 400 faster wall-clock time over the naive implementation, with comparable (and occasionally better) classification quality.

The amount of biological data publicly available has experienced an exponential growth as the technology advances. Online databases are now playing an important role as information repositories as well as easily accessible platforms for researchers to communicate and contribute. Recent research projects in image bioinformatics produce a number of databases of images, which visualize the spatial expression pattern of a gene (eg. "fj"), and most of which also have one or several annotation keywords (eg., "embryonic hindgut"). C-DEM is an online system for Drosophila (= fruit-fly) Embryo images Mining. It supports queries from all three modalities to all three, namely, (a) genes, (b) images of gene expression, and (c) annotation keywords of the images. Thus, it can find images that are similar to a given image, and/or related to the desirable annotation keywords, and/or related to specific genes. Typical queries are what are most suitable keywords to assign to image insitu28465.jpg or find images that are related to gene "fj", and to the keyword "embryonic hindgut" . C-DEM uses state-of-the-art feature extraction methods for images (wavelets and principal component analysis). It envisions the whole database as a tri-partite graph (one type for each modality), and it uses fast and flexible proximity measures, namely, random walk with restarts (RWR). In addition to flexible querying, C-DEM allows for navigation: the user can click on the results of an earlier query (image thumbnails and/or keywords and/or genes), and the system will report the most related images (and keywords, and genes). The demo is on a real Drosophila Embryo database, with 10,204 images, 2,969 distinct genes, and 113 annotation keywords. The query response time is below one second on a commodity desktop.