As users' preferences evolve over time, personalized online services increasingly rely on sequential recommender systems to predict future interactions by modeling patterns in historical user behavior. However, existing methods for sequential recommendation (SR) face two key challenges: they struggle to simultaneously leverage collaborative, semantic, and rating information, and the use of hard labels during training provides limited supervision. In this paper, we introduce SEAR, an LLM-powered Sequential recommEndation framework via fusion of collAborative, semantic, and Rating information. The proposed deep model comprises an embedding layer and a sequence encoder. The embedding layer transforms user-item interactions into three types of embeddings: collaborative, semantic, and rating. The sequence encoder then integrates these embeddings and identifies sequential patterns to model user representations. To enhance the utilization of item semantics, we integrate a large language model (LLM) to extract LLM embeddings. These embeddings are then employed to initialize the semantic embedding layer, collaborative embedding layer, and item embeddings. To capture more nuanced user behavior patterns, we generate preference-weighted soft labels based on the next k interactions. Extensive experiments validate the effectiveness of SEAR, and ablation studies further highlight the distinct contributions of the collaborative, semantic, and rating information.

Event forecasting is inherently influenced by multifaceted considerations, including international relations, regional historical dynamics, and cultural contexts. However, existing LLM-based approaches employ single-model architectures that generate predictions along a singular explicit trajectory, constraining their ability to capture diverse geopolitical nuances across complex regional contexts. To address this limitation, we introduce ThinkTank-ME, a novel Think Tank framework for Middle East event forecasting that emulates collaborative expert analysis in real-world strategic decision-making. To facilitate expert specialization and rigorous evaluation, we construct POLECAT-FOR-ME, a Middle East–focused event forecasting benchmark. Experimental results demonstrate the superiority of multi-expert collaboration in handling complex temporal geopolitical forecasting tasks. The code is available at https://github.com/LuminosityX/ThinkTank-ME.

Time series (TS) data are ubiquitous across various application areas, rendering time series forecasting (TSF) a fundamental task. With the astounding advances in large language models (LLMs), a variety of methods have been developed to adapt LLMs for time series forecasting. Despite unlocking the potential of LLMs in comprehending TS data, existing methods are inherently constrained by their shallow integration of TS information, wherein LLMs typically access TS representations at shallow layers, primarily at the input layer. This causes the influence of TS representations to progressively fade in deeper layers and eventually leads to ineffective adaptation between textual embeddings and TS representations. In this paper, we propose the Multi-layer Steerable Embedding Fusion (MSEF), a novel framework that enables LLMs to directly access time series patterns at all depths, thereby mitigating the progressive loss of TS information in deeper layers. Specifically, MSEF leverages off-the-shelf time series foundation models to extract semantically rich embeddings, which are fused with intermediate text representations across LLM layers via layer-specific steering vectors. These steering vectors are designed to continuously optimize the alignment between time series and textual modalities and facilitate a layer-specific adaptation mechanism that ensures efficient few-shot learning capabilities. Experimental results on seven benchmarks demonstrate significant performance improvements by MSEF compared with baselines, with an average reduction of 31.8% in terms of MSE. The code is available at https://github.com/One1sAll/MSEF.

Given a heterogeneous multilayer network with various connections in pharmacology, how can we detect components with intensive interactions and strong dependencies? Can we accurately capture suspicious groups in a multi-lot transaction network under camouflage? These challenges related to dense subgraph detection have been extensively studied in simple graphs but remain under-explored in complex networks. Existing methods struggle to effectively handle the intricate dependencies, let alone accurately identify the interrelated dense connected patterns within a series of complex heterogeneous networks. Here, we introduce INDUEN, a novel algorithm designed to detect interrelated densest subgraphs in multilayer networks by leveraging joint optimization of coupled factorization and local search for an elaborate-designed joint density measure. Experimental results demonstrate that INDUEN outperforms the state-of-the-art baselines in accurately detecting interrelated densest sub graphs under various settings. Furthermore, INDUEN uncovers some intriguing patterns in real-world data; it is linearly scalable and achieves more than 35 × speedup compared to the state-of-the-art method Destine.

The use of LLMs in natural language reasoning has shown mixed results, sometimes rivaling or even surpassing human performance in simpler classification tasks while struggling with social-cognitive reasoning, a domain where humans naturally excel. These differences have been attributed to many factors, such as variations in prompting and the specific LLMs used. However, no reasons appear conclusive, and no clear mechanisms have been established in prior work. In this study, we empirically evaluate how role-playing persona-based prompting influences Theory-of-Mind (ToM) reasoning capabilities. Grounding our research in psychological theory, we found that, beyond the inherent variance in the complexity of reasoning tasks, ToM performance differences arise because of socially-motivated prompting differences. In an era where prompt engineering with role-play is a typical approach to adapt LLMs to new contexts, our research advocates caution as models that adopt specific personas might potentially result in errors in social-cognitive reasoning.

Leveraging Large Language Models (LLMs) for generative recommendation has attracted significant research interest, where item tokenization is a critical step. It involves assigning item identifiers for LLMs to encode user history and generate the next item. Existing approaches leverage either token-sequence identifiers, representing items as discrete token sequences, or single-token identifiers, using ID or semantic embeddings. Token-sequence identifiers face issues such as the local optima problem in beam search and low generation efficiency due to step-by-step generation. In contrast, single-token identifiers fail to capture rich semantics or encode Collaborative Filtering (CF) information, resulting in suboptimal performance.

Generative AI search driven by large language models (LLMs) is reshaping information retrieval by offering end-to-end answers to complex queries, reducing users' reliance on manually browsing and summarizing multiple web pages. However, while this paradigm enhances convenience, it disrupts the feedback-driven improvement loop that has historically powered the evolution of traditional Web search. Web search can continuously improve their ranking models by collecting large-scale, fine-grained user feedback (e.g., clicks, dwell time) at the document level. In contrast, generative AI search operates through a much longer search pipeline-spanning query decomposition, document retrieval, and answer generation-yet typically receives only coarse-grained feedback on the final answer. This introduces a feedback loop disconnect, where user feedback for the final output cannot be effectively mapped back to specific system components, making it difficult to improve each intermediate stage and sustain the feedback loop.

Utilizing powerful Large Language Models (LLMs) for generative recommendation has attracted much attention. Nevertheless, a crucial challenge is transforming recommendation data into the language space of LLMs through effective item tokenization. Current approaches, such as ID, textual, and codebook-based identifiers, exhibit shortcomings in encoding semantic information, incorporating collaborative signals, or handling code assignment bias. To address these limitations, we propose LETTER (a LEarnable Tokenizer for generaTivE Recommendation), which integrates hierarchical semantics, collaborative signals, and code assignment diversity to satisfy the essential requirements of identifiers. LETTER incorporates Residual Quantized VAE for semantic regularization, a contrastive alignment loss for collaborative regularization, and a diversity loss to mitigate code assignment bias. We instantiate LETTER on two models and propose a ranking-guided generation loss to augment their ranking ability theoretically. Experiments on three datasets validate the superiority of LETTER, advancing the state-of-the-art in the field of LLM-based generative recommendation.

Harnessing Large Language Models (LLMs) for recommendation is rapidly emerging, which relies on two fundamental steps to bridge the recommendation item space and the language space: 1) item indexing utilizes identifiers to represent items in the language space, and 2) generation grounding associates LLMs' generated token sequences to in-corpus items. However, previous methods exhibit inherent limitations in the two steps. Existing ID-based identifiers (e.g., numeric IDs) and description-based identifiers (e.g., titles) either lose semantics or lack adequate distinctiveness. Moreover, prior generation grounding methods might generate invalid identifiers, thus misaligning with in-corpus items. To address these issues, we propose a novel Transition paradigm for LLM-based Recommender (named TransRec) to bridge items and language. Specifically, TransRec presents multi-facet identifiers, which simultaneously incorporate ID, title, and attribute for item indexing to pursue both distinctiveness and semantics. Additionally, we introduce a specialized data structure for TransRec to ensure generating valid identifiers only and utilize substring indexing to encourage LLMs to generate from any position of identifiers. Lastly, TransRec presents an aggregated grounding module to leverage generated multi-facet identifiers to rank in-corpus items efficiently. We instantiate TransRec on two backbone models, BART-large and LLaMA-7B.

Given a heterogeneous multilayer network with various connections in pharmacology, how can we detect components with intensive interactions and strong dependencies? Can we accurately capture suspicious groups in a multi-lot transaction network under camouflage? These challenges related to dense subgraph detection have been extensively studied in simple graphs (such as bipartite graph, multi-view network) but remain under-explored on complex networks. Existing methods struggle to effectively handle theintricate dependencies, let alone accurately identify theinterrelated dense connected patternswithin a series of complex heterogeneous networks. In this paper, we proposeInDuen, a novel algorithm designed to detect interrelated densest subgraphs in multilayer networks through joint optimization of coupled factorization and local search for an elaborate-designed joint density measure. It is(a)effective for both large synthetic and real networks,(b)resistant to camouflage for anomaly detection, and(c)linearly scalable. Experimental results demonstrate thatInDuenoutperforms the state-of-the-art baselines in accurately detecting interrelated densest subgraphs under various settings. Furthermore,InDuenuncovers some intriguing patterns in real-world data, i.e., closely cooperated academic groups and interrelated dependent functional components in biology-net.InDuenachieves more than$35 \times$speedup compared to the SOTA methodDestine.

Medical claims on social media, if left unchecked, have the potential to directly affect the well-being of consumers of online health information. However, existing studies on claim detection do not specifically focus on medical cure aspects, neither do they address if a cure claim is “checkworthy", an indicator of whether a claim is potentially beneficial or harmful, if unchecked. In this paper, we address these limitations by compiling CW-CURE, a novel dataset of CURE tweets, namely tweets containing claims on prevention, diagnoses, risks, treatments, and cures of medical conditions. CW-CURE contains tweets on four major health conditions, namely, Alzheimer’s disease, Cancer, Diabetes, and Depression annotated for claims, their “checkworthiness", as well as the different types of claims such as quantitative claim, correlation/causation, personal experience, and future prediction. We describe our processing pipeline for compiling CW-CURE and present classification results on CURE tweets using transformer-based models. In particular, we harness claim-type information obtained with zero-shot learning to show significant improvements in checkworthiness identification. Through CW-CURE, we hope to enable research on models for effective identification and flagging of impactful CURE content, to safeguard the public’s consumption of medical content online.

Sparsity is an established problem for the next Point-of-Interest (POI) recommendation task, where it hinders effective learning of user preferences from the User-POI matrix. However, learning multiple hierarchically related spatial tasks, and visiting relations between users and POIs, can help to alleviate this sparsity problem. In this article, we propose our Hierarchical Multi-Task Graph Recurrent Network (HMT-GRN) approach, which alleviates the sparsity problem by learning different User-Region matrices of lower sparsities in a multi-task setting. We then perform a Hierarchical Beam Search (HBS) on the different region and POI distributions to hierarchically reduce the search space with increasing spatial granularity and predict the next POI. Our HBS provides efficiency gains by reducing the search space, resulting in speedups of 5 to 7 times over an exhaustive approach. In addition, we propose a selectivity layer to predict if the next POI has been visited before by the user to balance between personalization and exploration. Further, we propose a novel Joint Triplet Loss Learning (JTLL) module to learn visited and unvisited relations between users and POIs for the recommendation task. Experimental results on two real-world Location-Based Social Network (LBSN) datasets show that our proposed approach significantly outperforms baseline and the state-of-the-art methods.

The emergence of fake media that can be easily created by technology has the potential to generate potent misinformation causing harm to both society and individuals. To tackle the issue, we have organized the Trusted Media Challenge (TMC) to explore how Artificial Intelligence (AI) technologies could be leveraged to combat fake media. To enable further research, we are releasing the dataset from the TMC, consists of 4,380 fake and 2,563 real videos, with various video and audio manipulation methods employed to produce different types of fake media. We have also carried out a user study to demonstrate the quality of the TMC dataset and to compare the performance of humans and AI models. The results show that the TMC dataset can fool human participants in many cases. The TMC dataset is available for research purposes upon request via [email protected]

Today's Cyber Physical Systems (CPSs) are large and complex data-intensive systems. Constant monitoring and analysis of the data generated by a multitude of interconnected sensors and actuators are required in order to detect anomalies due to possible intrusions or faults with high accuracy and timeliness. Recently, unsupervised anomaly detection techniques based on deep learning for multivariate time series have been proposed for detecting CPSs attacks with promising performance. However, the current methods are either limited by their representation learning methods in encoding the temporal and spatial information simultaneously and effectively, or cannot easily scale to other tasks without having explicit knowledge of the internal relationships between the different variables or sensors, which are both important for characterising CPSs data. In this paper, we propose a novel unsupervised anomaly detection method for multivariate time series MAD-SGCN which effectively captures the temporal and spatial correlations of the input sequences simultaneously using Long Short-Term Memory networks (LSTMs) and spectral-based Graph Convolutional Networks (GCNs). We design a self-supervised graph structure learning mechanism to minimize the usage of the prior knowledge about the network structures of the CPSs. Experiments on four CPS datasets demonstrate the superiority of the proposed method.

Learning which Point-of-Interest (POI) a user will visit next is a challenging task for personalized recommender systems due to the large search space of possible POIs in the region. A recurring problem among existing works that makes it difficult to learn and perform well is the sparsity of the User-POI matrix. In this paper, we propose our Hierarchical Multi-Task Graph Recurrent Network (HMT-GRN) approach, which alleviates the data sparsity problem by learning different User-Region matrices of lower sparsities in a multi-task setting. We then perform a Hierarchical Beam Search (HBS) on the different region and POI distributions to hierarchically reduce the search space with increasing spatial granularity and predict the next POI. Our HBS provides efficiency gains by reducing the search space, resulting in speedups of 5 to 7 times over an exhaustive approach. In addition, we also propose a novel selectivity layer to predict if the next POI has been visited before by the user to balance between personalization and exploration. Experimental results on two real-world Location-Based Social Network (LBSN) datasets show that our model significantly outperforms baseline and the state-of-the-art methods.

Static malware detection is important for protection against malware by allowing for malicious files to be detected prior to execution. It is also especially suitable for machine learning-based approaches. Recently, gradient boosting decision trees (GBDT) models, e.g., LightGBM (a popular implementation of GBDT), have shown outstanding performance for malware detection. However, as malware programs are known to evolve rapidly, malware classification models trained on the (source) training data often fail to generalize to the target domain, i.e., the deployed environment. To handle the underlying data distribution drifts, unsupervised domain adaptation techniques have been proposed for machine learning models including deep learning models. However, unsupervised domain adaptation for GBDT has remained challenging. In this paper, we adapt the adversarial learning framework for unsupervised domain adaptation to enable GBDT learn domain-invariant features and alleviate performance degradation in the target domain. In addition, to fully exploit the unlabelled target data, we merge them into the training dataset after pseudo-labelling. We propose a new weighting scheme integrated into GBDT for sampling instances in each boosting round to reduce the negative impact of wrongly labelled target instances. Experiments on two large malware datasets demonstrate the superiority of our proposed method.

Automatic inference of missing road attributes (e.g., road type and speed limit) for enriching digital maps has attracted significant research attention in recent years. A number of machine learning based approaches have been proposed to detect road attributes from GPS traces, dash-cam videos, or satellite images. However, existing solutions mostly focus on a single modality without modeling the correlations among multiple data sources. To bridge the gap, we present a multimodal road attribute detection method, which improves the robustness by performing pixel-level fusion of crowdsourced GPS traces and satellite images. A GPS trace is usually given by a sequence of location, bearing, and speed. To align it with satellite imagery in the spatial domain, we render GPS traces into a sequence of multi-channel images that simultaneously capture the global distribution of the GPS points, the local distribution of vehicles' moving directions and speeds, and their temporal changes over time, at each pixel. Unlike previous GPS based road feature extraction methods, our proposed GPS rendering does not require map matching in the data preprocessing step. Moreover, our multimodal solution addresses single-modal challenges such as occlusions in satellite images and data sparsity in GPS traces by learning the pixel-wise correspondences among different data sources. Extensive experiments have been conducted on two real-world datasets in Singapore and Jakarta. Compared with previous work, our method is able to improve the detection accuracy on road attributes by a large margin.

Unraveling human mobility patterns is critical for understanding disease spread and implementing effective controls during large-scale disease outbreaks such as the COVID-19 pandemic. Given the urgency associated with such situations, it is important to leverage on the common existing digital infrastructures that can be readily activated for disease outbreak analytics. We introduce an integrated system for disease outbreak investigation using data from Wi-Fi sessions. The system offers outbreak analytics, simulation, and visualization capabilities to assist in the identification of infection hot-spots and in contacttracing exercises. The system has been developed and experimentally deployed for research purposes on a large local university campus in Singapore.

Next destination recommendation is an important task in the transportation domain of taxi and ride-hailing services, where users are recommended with personalized destinations given their current origin location. However, recent recommendation works do not satisfy this origin-awareness property, and only consider learning from historical destination locations, without origin information. Thus, the resulting approaches are unable to learn and predict origin-aware recommendations based on the user's current location, leading to sub-optimal performance and poor real-world practicality. Hence, in this work, we study the origin-aware next destination recommendation task. We propose the Spatial-Temporal Origin-Destination Personalized Preference Attention (STOD-PPA) encoder-decoder model to learn origin-origin (OO), destination-destination (DD), and origin-destination (OD) relationships by first encoding both origin and destination sequences with spatial and temporal factors in local and global views, then decoding them through personalized preference attention to predict the next destination. Experimental results on seven real-world user trajectory taxi datasets show that our model significantly outperforms baseline and state-of-the-art methods.

Next Point-of-Interest (POI) recommendation is a longstanding problem across the domains of Location-Based Social Networks (LBSN) and transportation. Recent Recurrent Neural Network (RNN) based approaches learn POI-POI relationships in a local view based on independent user visit sequences. This limits the model's ability to directly connect and learn across users in a global view to recommend semantically trained POIs. In this work, we propose a Spatial-Temporal-Preference User Dimensional Graph Attention Network (STP-UDGAT), a novel explore-exploit model that concurrently exploits personalized user preferences and explores new POIs in global spatial-temporal-preference (STP) neighbourhoods, while allowing users to selectively learn from other users. In addition, we propose random walks as a masked self-attention option to leverage the STP graphs' structures and find new higher-order POI neighbours during exploration. Experimental results on six real-world datasets show that our model significantly outperforms baseline and state-of-the-art methods.

The quality of a digital map is of utmost importance for geo-aware services. However, maintaining an accurate and up-to-date map is a highly challenging task that usually involves a substantial amount of manual work. To reduce the manual efforts, methods have been proposed to automatically derive road attributes by mining GPS traces. However, previous methods always modeled each road attribute separately based on intuitive hand-crafted features extracted from GPS traces. This observation motivates us to propose a machine learning based method to learn joint features not only from GPS traces but also from map data. To model the relations among the target road attributes, we extract low-level shared feature embeddings via multi-task learning, while still being able to generate task-specific fused representations by applying attention-based feature fusion. To model the relations between the target road attributes and other contextual information that is available from a digital map, we propose to leverage map tiles at road centers as visual features that capture the information of the surrounding geographic objects around the roads. We perform extensive experiments on the OpenStreetMap where state-of-the-art classification accuracy has been obtained compared to existing road attribute detection approaches.

Conventional hypothesis testing is carried out in a hypothesis-driven manner. A scientist must first formulate a hypothesis based on what he or she sees and then devise a variety of experiments to test it. Given the rapid growth of data, it has become virtually impossible for a person to manually inspect all data to find all of the interesting hypotheses for testing. In this article, we propose and develop a data-driven framework for automatic hypothesis testing and analysis. We define a hypothesis as a comparison between two or more subpopulations. We find subpopulations for comparison using frequent pattern mining techniques and then pair them up for statistical hypothesis testing. We also generate additional information for further analysis of the hypotheses that are deemed significant. The number of hypotheses generated can be very large, and many of them are very similar. We develop algorithms to remove redundant hypotheses and present a succinct set of significant hypotheses to users. We conducted a set of experiments to show the efficiency and effectiveness of the proposed algorithms. The results show that our system can help users (1) identify significant hypotheses efficiently, (2) isolate the reasons behind significant hypotheses efficiently, and (3) find confounding factors that form Simpson’s paradoxes with discovered significant hypotheses.

This paper proposes a novel Integrated Oversampling (INOS) method that can handle highly imbalanced time series classification. We introduce an enhanced structure preserving oversampling (ESPO) technique and synergistically combine it with interpolation-based oversampling. ESPO is used to generate a large percentage of the synthetic minority samples based on multivariate Gaussian distribution, by estimating the covariance structure of the minority-class samples and by regularizing the unreliable eigen spectrum. To protect the key original minority samples, we use an interpolation-based technique to oversample a small percentage of synthetic population. By preserving the main covariance structure and intelligently creating protective variances in the trivial eigen dimensions, ESPO effectively expands the synthetic samples into the void area in the data space without being too closely tied with existing minority-class samples. This also addresses a key challenge for applying oversampling for imbalanced time series classification, i.e., maintaining the correlation between consecutive values through preserving the main covariance structure. Extensive experiments based on seven public time series data sets demonstrate that our INOS approach, used with support vector machines (SVM), achieved better performance over existing oversampling methods as well as state-of-the-art methods in time series classification.

We present a system called AssocExplorer to support exploratory data analysis via association rule visualization and exploration. AssocExplorer is designed by following the visual information-seeking mantra: overview first, zoom and filter, then details on demand. It effectively uses coloring to deliver information so that users can easily detect things that are interesting to them. If users find a rule interesting, they can explore related rules for further analysis, which allows users to find interesting phenomenon that are difficult to detect when rules are examined separately. Our system also allows users to compare rules and inspect rules with similar item composition but different statistics so that the key factors that contribute to the difference can be isolated.

Hypothesis testing is a well-established tool for scientific discovery. Conventional hypothesis testing is carried out in a hypothesis-driven manner. A scientist must first formulate a hypothesis based on his/her knowledge and experience, and then devise a variety of experiments to test it. Given the rapid growth of data, it has become virtually impossible for a person to manually inspect all the data to find all the interesting hypotheses for testing. In this paper, we propose and develop a data-driven system for automatic hypothesis testing and analysis. We define a hypothesis as a comparison between two or more sub-populations. We find sub-populations for comparison using frequent pattern mining techniques and then pair them up for statistical testing. We also generate additional information for further analysis of the hypotheses that are deemed significant. We conducted a set of experiments to show the efficiency of the proposed algorithms, and the usefulness of the generated hypotheses. The results show that our system can help users (1) identify significant hypotheses; (2) isolate the reasons behind significant hypotheses; and (3) find confounding factors that form Simpson's Paradoxes with discovered significant hypotheses.

This paper presents a novel structure preserving over sampling (SPO) technique for classifying imbalanced time series data. SPO generates synthetic minority samples based on multivariate Gaussian distribution by estimating the covariance structure of the minority class and regularizing the unreliable eigen spectrum. By preserving the main covariance structure and intelligently creating protective variances in the trivial eigen feature dimensions, the synthetic samples expand effectively into the void area in the data space without being too closely tied with existing minority-class samples. Extensive experiments based on several public time series datasets demonstrate that our proposed SPO in conjunction with support vector machines can achieve better performances than existing over sampling methods and state-of-the-art methods in time series classification.

The increasing infectious disease outbreaks has led to a need for new research to better understand the disease's origins, epidemiological features and pathogenicity caused by fast-mutating, fast-spreading viruses. Traditional sequence analysis methods do not take into account the spatio-temporal dynamics of rapidly evolving and spreading viral species. They are also focused on identifying single-point mutations. In this paper, we propose a novel approach that incorporates space-time relationships for studying changes in protein sequences from fast mutating viruses. We aim to detect both single-point mutations as well as k-mutations in the viral sequences. We define the problem of mutation chain pattern mining and design algorithms to discover valid mutation chains. Compact data structures to facilitate the mining process as well as pruning strategies to increase the scalability of the algorithms are devised. Experiments on both synthetic datasets and real world influenza A virus dataset show that our algorithms are scalable and effective in discovering mutations that occur geographically over time.

The availability and the accuracy of the data dictate the success of a data mining application. Increasingly, there is a need to resort to on-line data collection to address the problem of data availability. However, participants in on-line data collection applications are naturally distrustful of the data collector as well as their peer respondents, resulting in inaccurate data collected as the respondents refuse to provide truthful data in fear of collusion attacks. The current anonymity-preserving solutions for on-line data collection are unable to adequately resist such attacks in a scalable fashion. In this paper, we present an efficient anonymous data collection protocol for a malicious environment such as the Internet. The protocol employs cryptographic and random shuffling techniques to preserve participants' anonymity. The proposed method is collusion-resistant and guarantees that an attacker will be unable to breach an honest participant's anonymity unless she controls all N-1 participants. In addition, our method is efficient and achieved 15-42% communication overhead reduction in comparison to the prior state-of-the-art methods.

Learning from positive and unlabeled examples (PU learning) has been investigated in recent years as an alternative learning model for dealing with situations where negative training examples are not available. It has many real world applications, but it has yet to be applied in the data stream environment where it is highly possible that only a small set of positive data and no negative data is available. An important challenge is to address the issue of concept drift in the data stream environment, which is not easily handled by the traditional PU learning techniques. This paper studies how to devise PU learning techniques for the data stream environment. Unlike existing data stream classification methods that assume both positive and negative training data are available for learning, we propose a novel PU learning technique LELC (PU Learning by Extracting Likely positive and negative micro-Clusters) for document classification. LELC only requires a small set of positive examples and a set of unlabeled examples which is easily obtainable in the data stream environment to build accurate classifiers. Experimental results show that LELC is a PU learning method that can effectively address the issues in the data stream environment with significantly better speed and accuracy on capturing concept drift than the existing state-of-the-art PU learning techniques.

Biological networks such as the protein-protein interaction (PPI) network have been found to contain small recurring subnetworks in significantly higher frequencies than in random networks. Such network motifs are useful for uncovering structural design principles of complex biological networks. However, current network motif finding algorithms models the PPI network as a uni-labeled graph, discovering only unlabeled and thus relatively uninforma-tive network motifs as a result. Our objective is to exploit the currently available biological information that are associated with the vertices (the proteins) to capture not only the topological shapes of the motifs, but also the biological context in which they occurred in the PPI networks for network motif applications. We present a method called LaMoFinder to label network motifs with gene ontology terms in a PPI network. We also show how the resulting labeled network motifs can be used to predict unknown protein functions. Experimental results showed that the labeled network motifs extracted are biologically meaningful and can achieve better performance than existing PPI topology based methods for predicting unknown protein functions.

Recent works in network analysis have revealed the existence of network motifs in biological networks such as the protein-protein interaction (PPI) networks. However, existing motif mining algorithms are not sufficiently scalable to find meso-scale network motifs. Also, there has been little or no work to systematically exploit the extracted network motifs for dissecting the vast interactomes.We describe an efficient network motif discovery algorithm, NeMoFinder, that can mine meso-scale network motifs that are repeated and unique in large PPI networks. Using NeMoFinder, we successfully discovered, for the first time, up to size-12 network motifs in a large whole-genome S. cerevisiae (Yeast) PPI network. We also show that such network motifs can be systematically exploited for indexing the reliability of PPI data that were generated via highly erroneous high-throughput experimental methods.