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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.
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.
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: 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: Ahmed Anes Bendimerad
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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We address the problem of pattern discovery in vertex-attributed graphs. This kind of structure consists of a graph augmented with attributes associated to vertices. Vertex-attributed graphs provide a powerful abstraction that can be used to represent many datasets in an intuitive manner. Mining these graphs can be very useful for many applications. When mining vertex-attributed graphs, the principled integration of both graph and attribute data poses two important challenges. First, we need to define a pattern syntax that is intuitive and lends itself to efficient search. Considering that a pattern is generally defined over a subgraph, a pattern can be often huge in terms of vertices, which makes it difficult to grasp. Thus, the assimilation cost of a pattern is an important question that needs to be addressed. The second challenge is the formalization of the pattern interestingness. A pattern is generally relevant if it depicts some local properties that are somehow exceptional, otherwise, it will be already expected from the overall properties of the graph. Furthermore, the interestingness of patterns is subjective in practice, i.e., it significantly depends on the final user, her background knowledge and her preferences. Another common problem related to the interestingness of patterns is the redundancy issue in the result set. In other terms, a data mining approach may return a set of patterns that give redundant information, because these patterns cover very overlapping parts of vertices and attributes. We address these challenges for the problem of mining attributed graphs. We first introduce the task of discovering exceptional attributed subgraphs, which is rooted in Subgroup Discovery. The goal is to identify connected subgraphs whose vertices share characteristics that distinguish them from the rest of the graph. Then, we propose methods that aim to take into account the user and the domain knowledge when assessing the interestingness of patterns. We design a method that makes it possible to incorporate user's background knowledge and pattern's assimilation cost. This method is able to identify patterns that are both informative and easy to interpret. Furthermore, we propose another graph mining approach that integrates user's preferences. This method exploits an interactive process with the user to bias the pattern interestingness. It has been defined for the task of geo-located event detection in social media. Then, we design an approach that is able to incorporate hierarchical attribute dependencies into the pattern interestingness, which allows to avoid redundancy related to this kind of semantic relations between attributes. In other terms, when the attributes are organized as a hierarchy, this method is able to account for the inference that the user would make about some attribute values when she is informed about values of other attributes. Finally, we conclude this thesis by discussing some research perspectives.
Author: Simon M. Kaplan
Publisher:
ISBN:
Category : Computer programming
Languages : en
Pages : 40
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Author: Peng Lin
Publisher:
ISBN:
Category : Graphic methods
Languages : en
Pages : 0
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Book Description
Graph data quality is an emerging issue and is critical to graph analytics. Two graph quality issues are incompleteness (with missing links) and inconsistency (with conflicted attribute values). To mitigate these issues, link prediction is to infer the missing links that belong to the missing part of graphs, which improves the graph completeness; entity repair aims to detect the erroneous attribute values of nodes and change them to the correct values, which improves the graph consistency. This thesis proposes novel constraints with graph patterns incorporated and effective refinement algorithms for link prediction and entity repair in graphs, and further investigates a new problem of interactive graph refinement between multiple types of constraints with user feedback, such that it holistically enriches graph quality. (1) For link prediction, this thesis proposes a class of new data constraints namely GFCs, and develops efficient GFC discovery algorithms with performance guarantees and effective link prediction algorithms based on GFCs. Moreover, it extends GFCs with ontology, to further improve link prediction accuracy without sacrificing much efficiency. (2) For entity repair, novel constraints namely StarFDs are proposed to capture inconsistencies in graphs, by specifying graph functional dependencies with star structures. StarFDs enjoy efficient algorithms for error detection in attributed graphs. The repair problem aims to compute a graph that satisfies a set of StarFDs with minimum editing cost of attribute values, which is NP-hard and APX-hard. Despite the hardness, a dichotomous framework is proposed for the three cases of this problem that admits optimal, approximable, and bounded-cost solutions, all with performance guarantees. In addition, fundamental problems have been studied for StarFDs, i.e., satisfiability, validation, and implication. (3) An interactive graph refinement framework has been investigated, which integrates multiple constraints with user feedback holistically, to further enrich graph quality. This thesis proposes the system implementation for a user-centric graph refinement tool, which could impact the next generation of graph data management systems. It also shows that the proposed constraints can effectively benefit domain specific applications.
Author: Fabio Gadducci
Publisher: Springer Nature
ISBN: 3030789462
Category : Computers
Languages : en
Pages : 311
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Book Description
This book constitutes the refereed proceedings of the 14th International Conference on Graph Transformation, ICGT 2021, which took place virtually during June 24-25, 2021. The 14 full papers and 2 tool papers presented in this book were carefully reviewed and selected from 26 submissions. They deal with the following topics: theoretical advances; application domains; and tool presentations.
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: Heinrich Hussmann
Publisher: Springer Science & Business Media
ISBN: 3642145612
Category : Computers
Languages : en
Pages : 320
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Book Description
Model-Driven Development (MDD) has become an important paradigm in software development. It uses models as primary artifacts in the development process. This book provides an outstanding overview as well as deep insights into the area of model-driven development of user interfaces, which is an emerging topic in the intersection of Human-Computer-Interaction and Software-Engineering. The idea of this book is based on the very successful workshop series of “Model-Driven Development of Advanced User Interfaces (MDDAUI)”. It has been written by the leading researchers and practitioners in the field of model-driven development of user interfaces and offer a variety of solutions and examples for • Architectures and environments for the generation of user interfaces • User interface development for specific domains and purposes • Model-driven development in the context of ambient intelligence • Concepts supporting model-driven development of user interfaces
Author: Christopher James Love
Publisher:
ISBN:
Category :
Languages : en
Pages : 236
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Book Description
