Overlapping community detection in massive social networks

Massive social networks have become increasingly popular in recent years. Community detection is one of the most important techniques for the analysis of such complex networks. A community is a set of cohesive vertices that has more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. In this thesis, we propose scalable overlapping community detection algorithms that effectively identify high quality overlapping communities in various real-world networks. We first develop an efficient overlapping community detection algorithm using a seed set expansion approach. The key idea of this algorithm is to find good seeds and then greedily expand these seeds using a personalized PageRank clustering scheme. Experimental results show that our algorithm significantly outperforms other state-of-the-art overlapping community detection methods in terms of run time, cohesiveness of communities, and ground-truth accuracy. To develop more principled methods, we formulate the overlapping community detection problem as a non-exhaustive, overlapping graph clustering problem where clusters are allowed to overlap with each other, and some nodes are allowed to be outside of any cluster. To tackle this non-exhaustive, overlapping clustering problem, we propose a simple and intuitive objective function that captures the issues of overlap and non-exhaustiveness in a unified manner. To optimize the objective, we develop not only fast iterative algorithms but also more sophisticated algorithms using a low-rank semidefinite programming technique. Our experimental results show that the new objective and the algorithms are effective in finding ground-truth clusterings that have varied overlap and non-exhaustiveness. We extend our non-exhaustive, overlapping clustering techniques to co-clustering where the goal is to simultaneously identify a clustering of the rows as well as the columns of a data matrix. As an example application, consider recommender systems where users have ratings on items. This can be represented by a bipartite graph where users and items are denoted by two different types of nodes, and the ratings are denoted by weighted edges between the users and the items. In this case, co-clustering would be a simultaneous clustering of users and items. We propose a new co-clustering objective function and an efficient co-clustering algorithm that is able to identify overlapping clusters as well as outliers on both types of the nodes in the bipartite graph. We show that our co-clustering algorithm is able to effectively capture the underlying co-clustering structure of the data, which results in boosting the performance of a standard one-dimensional clustering. Finally, we study the design of parallel data-driven algorithms, which enables us to further increase the scalability of our overlapping community detection algorithms. Using PageRank as a model problem, we look at three algorithm design axes: work activation, data access pattern, and scheduling. We investigate the impact of different algorithm design choices. Using these design axes, we design and test a variety of PageRank implementations finding that data-driven, push-based algorithms are able to achieve a significantly superior scalability than standard PageRank implementations. The design choices affect both single-threaded performance as well as parallel scalability. The lessons learned from this study not only guide efficient implementations of many graph mining algorithms but also provide a framework for designing new scalable algorithms, especially for large-scale community detection.Computer Scienc

Inaccessible Entropy for Watermarking Generative Agents

In this work, we construct distortion-free and unforgeable watermarks for language models and generative agents. The watermarked output cannot be forged by a adversary nor removed by the adversary without significantly degrading model output quality. That is, the watermarked output is distortion-free: the watermarking algorithm does not noticeably change the quality of the model output and without the public detection key, no efficient adversary can distinguish output that is watermarked from outputs which are not. The core of the watermarking schemes involve embedding a message and publicly-verifiable digital signature in the generated model output. The message and signature can be extracted during the detection phase and verified by any authorized entity that has a public key. We show that, assuming the standard cryptographic assumption of one-way functions, we can construct distortion-free and unforgeable watermark schemes. Our framework relies on analyzing the inaccessible entropy of the watermarking schemes based on computational entropy notions derived from the existence of one-way functions

Machine learning applications for personalised automated radiotherapy planning

Automated radiotherapy planning is characterised by reduction in manual planning due to an increase in computerised planning. Current methods can produce plans suitable for clinical use. However, every case is unique and manual intervention is often needed. The goal of this work was to determine whether it is feasible to develop a fully automated planning system producing clinically optimal plans, and if so, to begin developing it. This work explored relationships between automated planning parameters and anatomical features with respect to dosimetric outcomes. A rules-based automated planning technique was used, an algorithm requiring calibration of input parameters prior to use. This calibration determines the target objectives the algorithm will optimise to. Existing calibration methods use a single set of calibrated parameters per treatment site and are applied to all patients. This approach is considered sufficient to meet clinical goals but may not be sufficient for development of optimal personalised planning due to anatomical variance between patients. Using a validated rules-based planning methodology and obtaining patient bespoke expert-driven calibrated parameters as the optimal gold standard and validation benchmark, two machine learning techniques were explored for apriori configuration of parameters for the delivery of personalised treatment planning. The main objective was to train models to predict gold standard parameters hence generating expert planning automatically. A secondary objective was to determine dosimetric differences between plans generated via machine learned parameters and a traditional single set of parameters applied to all cases. Preliminary studies were carried out to define what will be considered gold standard and to identify anatomical features for inclusion in the main study as well as their relationships to calibrated parameters. The research presented here was applied to three sites: prostate, rectum and lung. Findings are also expected to provide heuristics for research to be carried out on other treatment sites

The blessings of explainable AI in operations & maintenance of wind turbines

Wind turbines play an integral role in generating clean energy, but regularly suffer from operational inconsistencies and failures leading to unexpected downtimes and significant Operations & Maintenance (O&M) costs. Condition-Based Monitoring (CBM) has been utilised in the past to monitor operational inconsistencies in turbines by applying signal processing techniques to vibration data. The last decade has witnessed growing interest in leveraging Supervisory Control & Acquisition (SCADA) data from turbine sensors towards CBM. Machine Learning (ML) techniques have been utilised to predict incipient faults in turbines and forecast vital operational parameters with high accuracy by leveraging SCADA data and alarm logs. More recently, Deep Learning (DL) methods have outperformed conventional ML techniques, particularly for anomaly prediction. Despite demonstrating immense promise in transitioning to Artificial Intelligence (AI), such models are generally black-boxes that cannot provide rationales behind their predictions, hampering the ability of turbine operators to rely on automated decision making. We aim to help combat this challenge by providing a novel perspective on Explainable AI (XAI) for trustworthy decision support.This thesis revolves around three key strands of XAI – DL, Natural Language Generation (NLG) and Knowledge Graphs (KGs), which are investigated by utilising data from an operational turbine. We leverage DL and NLG to predict incipient faults and alarm events in the turbine in natural language as well as generate human-intelligible O&M strategies to assist engineers in fixing/averting the faults. We also propose specialised DL models which can predict causal relationships in SCADA features as well as quantify the importance of vital parameters leading to failures. The thesis finally culminates with an interactive Question- Answering (QA) system for automated reasoning that leverages multimodal domain-specific information from a KG, facilitating engineers to retrieve O&M strategies with natural language questions. By helping make turbines more reliable, we envisage wider adoption of wind energy sources towards tackling climate change

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining