Kernel Graph Convolutional Neural Networks

Graph kernels have been successfully applied to many graph classification problems. Typically, a kernel is first designed, and then an SVM classifier is trained based on the features defined implicitly by this kernel. This two-stage approach decouples data representation from learning, which is suboptimal. On the other hand, Convolutional Neural Networks (CNNs) have the capability to learn their own features directly from the raw data during training. Unfortunately, they cannot handle irregular data such as graphs. We address this challenge by using graph kernels to embed meaningful local neighborhoods of the graphs in a continuous vector space. A set of filters is then convolved with these patches, pooled, and the output is then passed to a feedforward network. With limited parameter tuning, our approach outperforms strong baselines on 7 out of 10 benchmark datasets.Comment: Accepted at ICANN '1

Εξόρυξη γνώσης από ροές κειμένων

Natural Language Processing (NLP) and Text Mining (TM) are active research fields that aim to derive information from text and transform the language used by humans to a form that a computer is able to comprehend. The semi-structured nature of text makes the task of mining information from text sources a very challenging task that after several decades is still an open research problem. Some of the most notable applications of text mining are ad hoc information retrieval, text categorization, keyword extraction, and document summarization.During the last decade the rise of the social media, news portals, websites as well as a variety of different applications resulted in massive streams of text data which are continuously produced over time. Many of the traditional text mining techniques cannot handle effectively or efficiently these streams and produce an output in real-time. My research during the last years focused on mining knowledge from text streams in real-time and focused on applications like keyword extraction, event detection, document similarity, and text summarization.Η Επεξεργασία Φυσικής Γλώσσας (NLP) και η Εξόρυξη Κειμένου (TM) είναι ερευνητικά πεδία που αποσκοπούν στην εξόρυξη πληροφορίας από κείμενο και στη μετατροπή της γλώσσα που χρησιμοποιούν οι άνθρωποι σε μια μορφή που ο υπολογιστής είναι σε θέση να κατανοήσει. Η ημι-δομημένη φύση του κειμένου καθιστά το έργο εξόρυξης πληροφοριών από πηγές κειμένου πολύ δύσκολο, το οποίο μετά από αρκετές δεκαετίες εξακολουθεί να είναι ένα ανοιχτό ερευνητικό πρόβλημα. Ορισμένες από τις πιο αξιοσημείωτες εφαρμογές εξόρυξης γνώσης είναι η ανάκτηση ad hoc πληροφοριών, η κατηγοριοποίηση κειμένου, η εξαγωγή λέξεων-κλειδιών και η περίληψη εγγράφων. Κατά την τελευταία δεκαετία η άνοδος των μέσων κοινωνικής δικτύωσης, των ιστότοπων καθώς και ποικίλων διαφορετικών εφαρμογών οδήγησε σε μαζικές ροές δεδομένων σε μορφή κειμένου που παράγονται συνεχώς με την πάροδο του χρόνου. Πολλές από τις παραδοσιακές τεχνικές εξόρυξης γνώσης δεν μπορούν να χειριστούν αποτελεσματικά αυτά τα ρεύματα σε πραγματικό χρόνο. Η έρευνά αυτή επικεντρώνεται στην εξόρυξη γνώσης από ροές κειμένου σε πραγματικό χρόνο

Matching Node Embeddings for Graph Similarity

Graph kernels have emerged as a powerful tool for graph comparison. Most existing graph kernels focus on local properties of graphs and ignore global structure. In this paper, we compare graphs based on their global properties as these are captured by the eigenvectors of their adjacency matrices. We present two algorithms for both labeled and unlabeled graph comparison. These algorithms represent each graph as a set of vectors corresponding to the embeddings of its vertices. The similarity between two graphs is then determined using the Earth Mover's Distance metric. These similarities do not yield a positive semidefinite matrix. To address for this, we employ an algorithm for SVM classification using indefinite kernels. We also present a graph kernel based on the Pyramid Match kernel that finds an approximate correspondence between the sets of vectors of the two graphs. We further improve the proposed kernel using the Weisfeiler-Lehman framework. We evaluate the proposed methods on several benchmark datasets for graph classification and compare their performance to state-of-the-art graph kernels. In most cases, the proposed algorithms outperform the competing methods, while their time complexity remains very attractive

Degeneracy-Based Real-Time Sub-Event Detection in Twitter Stream

In this paper, we deal with the task of sub-event detection in evolving events using posts collected from the Twitter stream. By representing a sequence of successive tweets in a short time interval as a weighted graph-of-words, we are able to identify the key moments (sub-events) that compose an event using the concept of graph degeneracy. We then select a tweet to best describe each sub-event using a simple yet effective heuristic. We evaluated our approach using humangenerated summaries containing the actual important sub-events within each event and compare it to two baseline approaches using several performance metrics such as DET curves and precision/recall performance. Extensive experiments on recent sporting event streams indicate that our approach outperforms the dominant sub-event detection methods and constructs a humanreadable event summary by aggregating the most representative tweets of each sub-event

Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization

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