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Author:
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ISBN:
Category :
Languages : en
Pages : 13
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Book Description
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 13
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Book Description
Author:
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ISBN:
Category :
Languages : en
Pages : 4
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Author: Amin Salehi
Publisher:
ISBN:
Category : Graph theory
Languages : en
Pages : 91
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Book Description
Graph is a ubiquitous data structure, which appears in a broad range of real-world scenarios. Accordingly, there has been a surge of research to represent and learn from graphs in order to accomplish various machine learning and graph analysis tasks. However, most of these efforts only utilize the graph structure while nodes in real-world graphs usually come with a rich set of attributes. Typical examples of such nodes and their attributes are users and their profiles in social networks, scientific articles and their content in citation networks, protein molecules and their gene sets in biological networks as well as web pages and their content on the Web. Utilizing node features in such graphs---attributed graphs---can alleviate the graph sparsity problem and help explain various phenomena (e.g., the motives behind the formation of communities in social networks). Therefore, further study of attributed graphs is required to take full advantage of node attributes. In the wild, attributed graphs are usually unlabeled. Moreover, annotating data is an expensive and time-consuming process, which suffers from many limitations such as annotators' subjectivity, reproducibility, and consistency. The challenges of data annotation and the growing increase of unlabeled attributed graphs in various real-world applications significantly demand unsupervised learning for attributed graphs. In this dissertation, I propose a set of novel models to learn from attributed graphs in an unsupervised manner. To better understand and represent nodes and communities in attributed graphs, I present different models in node and community levels. In node level, I utilize node features as well as the graph structure in attributed graphs to learn distributed representations of nodes, which can be useful in a variety of downstream machine learning applications. In community level, with a focus on social media, I take advantage of both node attributes and the graph structure to discover not only communities but also their sentiment-driven profiles and inter-community relations (i.e., alliance, antagonism, or no relation). The discovered community profiles and relations help to better understand the structure and dynamics of social media.
Author: Junteng Jia
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Graphs are a natural representation for systems with interacting components (e.g. an online social network of users; a transaction network of bank accounts; an interaction network of proteins). As such, algorithms that predict node labels have wide-ranged applications from online content recommendation, fraud detection, to drug discovery. The traditional machine learning setting assumes data points are independently sampled, and thus makes predictions only based on each individual's attributes. For interconnected vertices in an attributed graph, the correlation along the edges provide an additional source of information. To better understand and leverage those two types of information, we propose data models for attributed graphs that: (1) explain existing graph learning algorithms such as label propagation and graph convolutional network, (2) inspire new algorithms that achieves the state-of-the-art performances, (3) generate synthetic graph attributes that preserves characteristics in real-world data.
Author: Chao Han
Publisher:
ISBN:
Category :
Languages : en
Pages : 118
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Book Description
In various domains, such as information retrieval, earth science, remote sensing and social network, vast amounts of data can be viewed as attributed graphs as they are associated with attributes which describe the property of data and structure which reflects the inter-dependency among variables in the data. Given the broad coverage and the unique representation of attributed graphs, many studies with a focus on predictive modeling have been conducted. For example, node prediction aims at predicting the attributes of nodes; link prediction aims at predicting the graph structure; graph prediction aims at predicting the attributes from the entire graph. To provide better predictive modeling, we need to gain deep insights from the principle elements of the attributed graph. In this thesis, we explore answers to three open questions: (1) how to discover the structure of the graph efficiently? (2) how to find a compact and lossless representation of the attributes of the graph? (3) how to exploit the temporal contexts exhibited in the graph? For structure learning, we first propose a structure learning method which is capable of modeling the nonlinear relationship between attributes and target variables. The method is more effective than alternative approaches which are without nonlinear modeling or structure learning on the task of graph regression. It however suffers from the high computational cost brought from the structure learning. To address this limitation, we then propose a conditional dependency network which can discover the graph structure in a distributed manner. The experimental results suggest that this method is much more efficient than other methods while being comparable in terms of effectiveness. For representation learning, we introduced a Structure-Aware Intrinsic Representation Learning model. Different from existing methods which only focus on learning the compact representation of the target space of the attributed graph. Our method can jointly learn lower dimensional embeddings of the target space and feature space via structure-aware graph abstraction and feature-aware target embedding learning. The results indicate that the embedding produced from the proposed method is better than the ones from alternative state-of-the-art embedding learning methods across all experimental settings. For temporal modeling, we introduced a time-aware neural attentive model to capture the temporal dynamics exhibited in session-based news recommendation, in which the user's sequential behaviors are attributed graphs with chain structure and temporal contexts as attributes. The unique temporal dynamics specific to news include: readers' interests shift over time, readers comment irregularly on articles, and articles are perishable items with limited lifespans. The result demonstrates the effectiveness of our method against a number of state-of-the-art methods on several real-world news datasets.
Author: Holger Giese
Publisher: Universitätsverlag Potsdam
ISBN: 3869564334
Category : Computers
Languages : en
Pages : 36
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Book Description
Various kinds of typed attributed graphs are used to represent states of systems from a broad range of domains. For dynamic systems, established formalisms such as graph transformations provide a formal model for defining state sequences. We consider the extended case where time elapses between states and introduce a logic to reason about these sequences. With this logic we express properties on the structure and attributes of states as well as on the temporal occurrence of states that are related by their inner structure, which no formal logic over graphs accomplishes concisely so far. Firstly, we introduce graphs with history by equipping every graph element with the timestamp of its creation and, if applicable, its deletion. Secondly, we define a logic on graphs by integrating the temporal operator until into the well-established logic of nested graph conditions. Thirdly, we prove that our logic is equally expressive to nested graph conditions by providing a suitable reduction. Finally, the implementation of this reduction allows for the tool-based analysis of metric temporal properties for state sequences. Verschiedene Arten von getypten attributierten Graphen werden benutzt, um Zustände von Systemen in vielen unterschiedlichen Anwendungsbereichen zu beschreiben. Der etablierte Formalismus der Graphtransformationen bietet ein formales Model, um Zustandssequenzen für dynamische Systeme zu definieren. Wir betrachten den erweiterten Fall von solchen Sequenzen, in dem Zeit zwischen zwei verschiedenen Systemzuständen vergeht, und führen eine Logik ein, um solche Sequenzen zu beschreiben. Mit dieser Logik drücken wir zum einen Eigenschaften über die Struktur und die Attribute von Zuständen aus und beschreiben zum anderen temporale Vorkommen von Zuständen, die durch ihre innere Struktur verbunden sind. Solche Eigenschaften können bisher von keiner der existierenden Logiken auf Graphen vergleichbar darstellt werden. Erstens führen wir Graphen mit Änderungshistorie ein, indem wir jedes Graphelement mit einem Zeitstempel seiner Erzeugung und, wenn nötig, seiner Löschung versehen. Zweitens definieren wir eine Logik auf Graphen, indem wir den Temporaloperator Until in die wohl-etablierte Logik der verschachtelten Graphbedingungen integrieren. Drittens beweisen wir, dass unsere Logik gleich ausdrucksmächtig ist, wie die Logik der verschachtelten Graphbedingungen, indem wir eine passende Reduktionsoperation definieren. Zuletzt erlaubt uns die Implementierung dieser Reduktionsoperation die werkzeukbasierte Analyse von metrisch-temporallogischen Eigenschaften für Zustandssequenzen zu führen.
Author: Dean Lusher
Publisher: Cambridge University Press
ISBN: 0521193567
Category : Business & Economics
Languages : en
Pages : 361
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Book Description
This book provides an account of the theoretical and methodological underpinnings of exponential random graph models (ERGMs).
Author: Hartmut Ehrig
Publisher: Springer
ISBN: 366247980X
Category : Computers
Languages : en
Pages : 468
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Book Description
This book is a comprehensive explanation of graph and model transformation. It contains a detailed introduction, including basic results and applications of the algebraic theory of graph transformations, and references to the historical context. Then in the main part the book contains detailed chapters on M-adhesive categories, M-adhesive transformation systems, and multi-amalgamated transformations, and model transformation based on triple graph grammars. In the final part of the book the authors examine application of the techniques in various domains, including chapters on case studies and tool support. The book will be of interest to researchers and practitioners in the areas of theoretical computer science, software engineering, concurrent and distributed systems, and visual modelling.
Author: Chuan Shi
Publisher: Springer Nature
ISBN: 9811661669
Category : Computers
Languages : en
Pages : 329
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Book Description
Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node. In this book, we provide a comprehensive survey of current developments in HG representation learning. More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.
Author: Qi Song
Publisher:
ISBN:
Category : Graphic methods
Languages : en
Pages : 236
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Book Description
Attributed graphs are now been widely used in expressing world wide web, social network, knowledge base, biological structure, etc. Analyzing heterogeneous and large-scale graphs is expensive and writing queries to search entities or rank nodes is nevertheless a nontrivial task for end users. It is hard for end-users to write precise queries that will lead to meaningful answers without any prior knowledge of the underlying data graph. Users often need to revise the queries multiple times to find desirable answers. Given a large number of entities in the graph, users often require efficient predictive models that can effectively suggest the nodes as answers for analytical queries. Analyzing such graphs is challenging due to the ambiguity in queries, the inherent computational complexity (e.g., subgraph isomorphism) and resource constraints (e.g., response time) for large graphs. In order to solve these challenges, the dissertation focuses on the problem of attributed graph analysis with usability. It solves two typical graph analysis tasks, entity search and node ranking. We provide usability for attributed graph analysis which contains (1) a query construction method that helps users to write precise queries without any specific query languages, (2) an optimization strategy that improves the query evaluation process, (3) a mechanism that helps users to understand the result and fine-tune the queries, and (4) a supervised model that captures users' interest and automatically rank entities. The thesis experimentally verifies the efficiency and effectiveness of the proposed approaches using real-life graphs.
