LEARNING ON GRAPHS: ALGORITHMS FOR CLASSIFICATION AND SEQUENTIAL DECISIONS

In recent years, networked data have become widespread due to the increasing importance of social networks and other web-related applications. This growing interest is driving researchers to design new algorithms for solving important problems that involve networked data. In this thesis we present a few practical yet principled algorithms for learning and sequential decision-making on graphs. Classification of networked data is an important problem that has recently received a great deal of attention from the machine learning community. This is due to its many important practical applications: computer vision, bioinformatics, spam detection and text categorization, just to cite a few of the more conspicuous examples. We focus our attention on the task called ``node classification'', often studied in the semi-supervised (transductive) setting. We present two algorithms, motivated by different theoretical frameworks. The first algorithm is studied in the well-known online adversarial setting, within which it enjoys an optimal mistake bound (up to logarithmic factors). The second algorithm is based on a game-theoretic approach, where each node of the network is maximizing its own payoff. The setting corresponds to a Graph Transduction Game in which the graph is a tree. For this special case, we show that the Nash Equilibrium of the game can be reached in linear time. We complement our theoretical findings with an extensive set of experiments using datasets from many different domains. In the second part of the thesis, we present a rapidly emerging theme in the analysis of networked data: signed networks, graphs whose edges carry a label encoding the positive or negative nature of the relationship between the connected nodes. For example, social networks and e-commerce offer several examples of signed relationships: Slashdot users can tag other users as friends or foes, Epinions users can rate each other positively or negatively, Ebay users develop trust and distrust towards sellers in the network. More generally, two individuals that are related because they rate similar products in a recommendation website may agree or disagree in their ratings. Many heuristics for link classification in social networks are based on a form of social balance summarized by the motto \u201cthe enemy of my enemy is my friend\u201d. This is equivalent to saying that the signs on the edges of a social graph tend to be consistent with some two-clustering structure of the nodes, where edges connecting nodes from the same cluster are positive and edges connecting nodes from different clusters are negative. We present algorithms for the batch transductive active learning setting, where the topology of the graph is known in advance and our algorithms can ask for the label of some specific edges during the training phase (before starting with the predictions). These algorithms can achieve different tradeoffs between the number of mistakes during the test phase and the number of labels required during the training phase. We also presented an experimental comparison against some state-of-the-art spectral heuristics presented in a previous work, where we show that the simplest or our algorithms is already competitive with the best of these heuristics. In the last chapter we present another way to exploit relational information for sequential predictions: the networks of bandits. Contextual bandits adequately formalize the exploration-exploitation trade-offs arising in several industrially relevant applications, such online advertisement and recommendation systems. Many practical applications have a strong social component whose integration in the bandit algorithm could lead to a significant performance improvement: for example, since often friends have similar taste, we may want to serve contents to a group of users by taking advantage of an underlying network of social relationships among them. We introduce a novel algorithmic approach to a particular networked bandit problem. More specifically, we run a bandit algorithm on each network node (e.g., user), allowing it to ``share'' feedback signals with the other nodes by employing the multi-task kernel. We derive the regret analysis of this algorithm and, finally, we report on the results of an experimental comparison between our approach and the state of the art techniques, on both artificial and real-world social networks

Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation

Joint segmentation and classification of fine-grained actions is important for applications of human-robot interaction, video surveillance, and human skill evaluation. However, despite substantial recent progress in large-scale action classification, the performance of state-of-the-art fine-grained action recognition approaches remains low. We propose a model for action segmentation which combines low-level spatiotemporal features with a high-level segmental classifier. Our spatiotemporal CNN is comprised of a spatial component that uses convolutional filters to capture information about objects and their relationships, and a temporal component that uses large 1D convolutional filters to capture information about how object relationships change across time. These features are used in tandem with a semi-Markov model that models transitions from one action to another. We introduce an efficient constrained segmental inference algorithm for this model that is orders of magnitude faster than the current approach. We highlight the effectiveness of our Segmental Spatiotemporal CNN on cooking and surgical action datasets for which we observe substantially improved performance relative to recent baseline methods.Comment: Updated from the ECCV 2016 version. We fixed an important mathematical error and made the section on segmental inference cleare

A correlation clustering approach to link classification in signed networks

Motivated by social balance theory, we develop a theory of link classification in signed networks using the correlation clustering index as measure of label regularity. We derive learning bounds in terms of correlation clustering within three fundamental transductive learning settings: online, batch and active. Our main algorithmic contribution is in the active setting, where we introduce a new family of efficient link classifiers based on covering the input graph with small circuits. These are the first active algorithms for link classification with mistake bounds that hold for arbitrary signed networks

Preserved speech variant is allelic of classic Rett syndrome

Rett syndrome is a neurological disorder affecting predominantly females with regression loss of speech and purposeful hand use, after a few months of almost normal development. Postnatal microcephaly, hand dispraxia, stereotypic 'hand-washing' activities, ataxia, and abnormal breathing are among its most characteristic features. Another aspect of this disorder is growth failure. The preserved speech variant (PSV) shares with Rett syndrome the same course and the stereotypic hand-washing activities but it differs in that patients typically recover some degree of speech and hand use and usually do not show growth failure. Progressive scoliosis, epilepsy and other minor handicaps, usually present in Rett syndrome, are rare in the preserved speech variant. Here we explore the spectrum of mutations affecting the MECP2 gene in a group of 25 classic Rett syndrome girls and in three patients with the preserved speech variant. Among the Rett syndrome group, two novel mutational hot spots (R270X and R294X), four novel mutations, two novel small deletions, as well as the previously reported 806delG, R168X and R255X mutations, were identified in 20/25 patients. Of note, among the preserved speech variants, two patients carry deletions of 41 bp and 44 bp each, which are strikingly similar to those observed in classic Rett syndrome. Our results confirm the presence of mutational hot spots in MECP2, broaden the spectrum of mutations, pinpoint additional mutational hot spots and establish that the preserved speech variant is indeed allelic of the classic form. Phenotype variability is only partially dependent on the kind of MECP2 mutation and other mechanisms such as skewed X-inactivation, and/or modifier gene effects should be investigated to explain the variable recovery in speech and hand use

Exploratory factor analysis of graphical features for link prediction in social networks

Social Networks attract much attention due to their ability to replicate social interactions at scale. Link prediction, or the assessment of which unconnected nodes are likely to connect in the future, is an interesting but non-trivial research area. Three approaches exist to deal with the link prediction problem: feature-based models, Bayesian probabilistic models, probabilistic relational models. In feature-based methods, graphical features are extracted and used for classification. Usually, these features are subdivided into three feature groups based on their formula. Some formulas are extracted based on neighborhood graph traverse. Accordingly, there exists three groups of features, neighborhood features, path-based features, node-based features. In this paper, we attempt to validate the underlying structure of topological features used in feature-based link prediction. The results of our analysis indicate differing results from the prevailing grouping of these features, which indicates that current literatures\u27 classification of feature groups should be redefined. Thus, the contribution of this work is exploring the factor loading of graphical features in link prediction in social networks. To the best of our knowledge, there is no prior studies had addressed it

Germline mosaicism in Rett syndrome identified by prenatal diagnosis

Rett syndrome is an X-linked neurodevelopmental dominant disorder that affects almost exclusively girls. The vast majority of cases are sporadic and are caused by de novo mutations in the MECP2 gene, located in Xq28. Only few familial cases have been reported: in four cases, the mother was an asymptomatic carrier and in other four cases, the germline mosaicism in the mother was postulated. Owing to the above reported cases of germline mosaicism, we decided to offer prenatal diagnosis to all expectant mothers with a Rett daughter despite the absence of the causative mutation in parents' blood. We describe here the outcome of the first nine cases of prenatal diagnosis followed by our center. In eight cases, the fetus did not carry the mutation. In one case, the female fetus did carry the same mutation of the affected sister. The couple decided to interrupt the pregnancy and to devolve fetal tissues for research purposes. Our results indicate that prenatal diagnosis should be proposed to all couples with a Rett daughter, even when the mutation is apparently de novo. Moreover, one positive prenatal test among the first nine cases indicates that germline mosaicism may be seriously considered for the assessment of recurrence risk during genetic counseling

Risk Factors for Intra-Abdominal Candidiasis in Intensive Care Units: Results from EUCANDICU Study

Introduction: Intra-abdominal infections represent the second most frequently acquired infection in the intensive care unit (ICU), with mortality rates ranging from 20% to 50%. Candida spp. may be responsible for up to 10–30% of cases. This study assesses risk factors for development of intra-abdominal candidiasis (IAC) among patients admitted to ICU. Methods: We performed a case–control study in 26 European ICUs during the period January 2015–December 2016. Patients at least 18 years old who developed an episode of microbiologically documented IAC during their stay in the ICU (at least 48 h after admission) served as the case cohort. The control group consisted of adult patients who did not develop episodes of IAC during ICU admission. Matching was performed at a ratio of 1:1 according to time at risk (i.e. controls had to have at least the same length of ICU stay as their matched cases prior to IAC onset), ICU ward and period of study. Results: During the study period, 101 case patients with a diagnosis of IAC were included in the study. On univariate analysis, severe hepatic failure, prior receipt of antibiotics, prior receipt of parenteral nutrition, abdominal drain, prior bacterial infection, anastomotic leakage, recurrent gastrointestinal perforation, prior receipt of antifungal drugs and higher median number of abdominal surgical interventions were associated with IAC development. On multivariate analysis, recurrent gastrointestinal perforation (OR 13.90; 95% CI 2.65–72.82, p = 0.002), anastomotic leakage (OR 6.61; 95% CI 1.98–21.99, p = 0.002), abdominal drain (OR 6.58; 95% CI 1.73–25.06, p = 0.006), prior receipt of antifungal drugs (OR 4.26; 95% CI 1.04–17.46, p = 0.04) or antibiotics (OR 3.78; 95% CI 1.32–10.52, p = 0.01) were independently associated with IAC. Conclusions: Gastrointestinal perforation, anastomotic leakage, abdominal drain and prior receipt of antifungals or antibiotics may help to identify critically ill patients with higher probability of developing IAC. Prospective studies are needed to identify which patients will benefit from early antifungal treatment

Cognition and Behaviour in Sotos Syndrome: A Systematic Review

BACKGROUND:Research investigating cognition and behaviour in Sotos syndrome has been sporadic and to date, there is no published overview of study findings. METHOD:A systematic review of all published literature (1964-2015) presenting empirical data on cognition and behaviour in Sotos syndrome. Thirty four journal articles met inclusion criteria. Within this literature, data relating to cognition and/or behaviour in 247 individuals with a diagnosis of Sotos syndrome were reported. Ten papers reported group data on cognition and/or behaviour. The remaining papers employed a case study design. RESULTS:Intelligence quotient (IQ) scores were reported in twenty five studies. Intellectual disability (IQ < 70) or borderline intellectual functioning (IQ 70-84) was present in the vast majority of individuals with Sotos syndrome. Seven studies reported performance on subscales of intelligence tests. Data from these studies indicate that verbal IQ scores are consistently higher than performance IQ scores. Fourteen papers provided data on behavioural features of individuals with Sotos syndrome. Key themes that emerged in the behavioural literature were overlap with ASD, ADHD, anxiety and high prevalence of aggression/tantrums. CONCLUSION:Although a range of studies have provided insight into cognition and behaviour in Sotos syndrome, specific profiles have not yet been fully specified. Recommendations for future research are provided