Relevant bibliographies by topics / Search in graphs AND

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Search in graphs AND'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Search in graphs AND"

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Itzhakov, Avraham, and Michael Codish. "Incremental Symmetry Breaking Constraints for Graph Search Problems." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 1536–43. http://dx.doi.org/10.1609/aaai.v34i02.5513.

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This paper introduces incremental symmetry breaking constraints for graph search problems which are complete and compact. We show that these constraints can be computed incrementally: A symmetry breaking constraint for order n graphs can be extended to one for order n + 1 graphs. Moreover, these constraints induce a special property on their canonical solutions: An order n canonical graph contains a canonical subgraph on the first k vertices for every 1 ≤ k ≤ n. This facilitates a “generate and extend” paradigm for parallel graph search problem solving: To solve a graph search problem φ on order n graphs, first generate the canonical graphs of some order k < n. Then, compute canonical solutions for φ by extending, in parallel, each canonical order k graph together with suitable symmetry breaking constraints. The contribution is that the proposed symmetry breaking constraints enable us to extend the order k canonical graphs to order n canonical solutions. We demonstrate our approach through its application on two hard graph search problems.
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Yang, Jianye, Wu Yao, and Wenjie Zhang. "Keyword Search on Large Graphs: A Survey." Data Science and Engineering 6, no. 2 (2021): 142–62. http://dx.doi.org/10.1007/s41019-021-00154-4.

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AbstractWith the prevalence of Internet access and online services, various big graphs are generated in many real applications (e.g., online social networks and knowledge graphs). An important task on analyzing and mining these graphs is keyword search. Essentially, given a graph G and query Q associated with a set of keywords, the keyword search aims to find a substructure (e.g., rooted tree or subgraph) S in G such that nodes in S collectively cover part of or all keywords in Q, and in the meanwhile, S is optimal on some user specified semantics. Keyword search on graphs can be applied in many real-life applications, such as point-of-interests recommendation and web search facility. In spite of the great importance of graph keyword search, we, however, notice that the latest survey on this topic is far out of date. Consequently, there is prompt need to conduct a comprehensive survey in this research direction. Motivated by this, in this survey, we systematically review graph keyword search studies by classifying the existing works into different categories according to the specific problem definition. This survey aims to provide the researchers a comprehensive understanding of existing graph keyword search solutions.
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Beamer, Scott, Krste Asanović, and David Patterson. "Direction-Optimizing Breadth-First Search." Scientific Programming 21, no. 3-4 (2013): 137–48. http://dx.doi.org/10.1155/2013/702694.

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Breadth-First Search is an important kernel used by many graph-processing applications. In many of these emerging applications of BFS, such as analyzing social networks, the input graphs are low-diameter and scale-free. We propose a hybrid approach that is advantageous for low-diameter graphs, which combines a conventional top-down algorithm along with a novel bottom-up algorithm. The bottom-up algorithm can dramatically reduce the number of edges examined, which in turn accelerates the search as a whole. On a multi-socket server, our hybrid approach demonstrates speedups of 3.3–7.8 on a range of standard synthetic graphs and speedups of 2.4–4.6 on graphs from real social networks when compared to a strong baseline. We also typically double the performance of prior leading shared memory (multicore and GPU) implementations.
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Kargar, Mehdi, and Aijun An. "Keyword search in graphs." Proceedings of the VLDB Endowment 4, no. 10 (2011): 681–92. http://dx.doi.org/10.14778/2021017.2021025.

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Lunegov, S. V., and N. N. Petrov. "Search Problems on Graphs." IFAC Proceedings Volumes 34, no. 6 (2001): 969–71. http://dx.doi.org/10.1016/s1474-6670(17)35307-7.

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Aigner, M. "Search problems on graphs." Discrete Applied Mathematics 14, no. 3 (1986): 215–30. http://dx.doi.org/10.1016/0166-218x(86)90026-0.

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Zainullina, R. "Automatic Graph Generation for E-learning Systems." Bulletin of Science and Practice 7, no. 6 (2021): 12–16. http://dx.doi.org/10.33619/2414-2948/67/01.

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The subject of the research is one of the ways of updating modern training systems for solving problems of graph theory, namely, automatic generation of graphs. This approach will reduce the load on the training system database and generate tasks for the user in real-time without updating the bank of tasks. In the course of the work, the advantages and disadvantages of this approach were identified. The most suitable method for the implementation of the research was chosen to represent graphs in electronic computers. The requirements for generated graphs and possible ways of implementing these requirements are identified and substantiated. Namely: in the implemented program, simple connected undirected graphs will be generated. We considered an important detail in working with graphs — graph traversal using the “Depth (width) search” algorithm, which in this task is used to check the graph for connectivity. The result of the work is presented — a software implementation of the graph generation algorithm in the C# programming language. In it, graphs are represented by an adjacency list, generated randomly, and checked for connectivity using the DFS (Depth First Search) function. DFS is a software implementation of the Depth First Search algorithm.
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Burkhardt, Paul. "Optimal Algebraic Breadth-First Search for Sparse Graphs." ACM Transactions on Knowledge Discovery from Data 15, no. 5 (2021): 1–19. http://dx.doi.org/10.1145/3446216.

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There has been a rise in the popularity of algebraic methods for graph algorithms given the development of the GraphBLAS library and other sparse matrix methods. An exemplar for these approaches is Breadth-First Search (BFS). The algebraic BFS algorithm is simply a recurrence of matrix-vector multiplications with the n × n adjacency matrix, but the many redundant operations over nonzeros ultimately lead to suboptimal performance. Therefore an optimal algebraic BFS should be of keen interest especially if it is easily integrated with existing matrix methods. Current methods, notably in the GraphBLAS, use a Sparse Matrix masked-Sparse Vector multiplication in which the input vector is kept in a sparse representation in each step of the BFS, and nonzeros in the vector are masked in subsequent steps. This has been an area of recent research in GraphBLAS and other libraries. While in theory, these masking methods are asymptotically optimal on sparse graphs, many add work that leads to suboptimal runtime. We give a new optimal, algebraic BFS for sparse graphs, thus closing a gap in the literature. Our method multiplies progressively smaller submatrices of the adjacency matrix at each step. Let n and m refer to the number of vertices and edges, respectively. On a sparse graph, our method takes O ( n ) algebraic operations as opposed to O ( m ) operations needed by theoretically optimal sparse matrix approaches. Thus, for sparse graphs, it matches the bounds of the best-known sequential algorithm, and on a Parallel Random Access Machine, it is work-optimal. Our result holds for both directed and undirected graphs. Compared to a leading GraphBLAS library, our method achieves up to 24x faster sequential time, and for parallel computation, it can be 17x faster on large graphs and 12x faster on large-diameter graphs.
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Cvetkovic, Dragos. "Spectral recognition of graphs." Yugoslav Journal of Operations Research 22, no. 2 (2012): 145–61. http://dx.doi.org/10.2298/yjor120925025c.

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At some time, in the childhood of spectral graph theory, it was conjectured that non-isomorphic graphs have different spectra, i.e. that graphs are characterized by their spectra. Very quickly this conjecture was refuted and numerous examples and families of non-isomorphic graphs with the same spectrum (cospectral graphs) were found. Still some graphs are characterized by their spectra and several mathematical papers are devoted to this topic. In applications to computer sciences, spectral graph theory is considered as very strong. The benefit of using graph spectra in treating graphs is that eigenvalues and eigenvectors of several graph matrices can be quickly computed. Spectral graph parameters contain a lot of information on the graph structure (both global and local) including some information on graph parameters that, in general, are computed by exponential algorithms. Moreover, in some applications in data mining, graph spectra are used to encode graphs themselves. The Euclidean distance between the eigenvalue sequences of two graphs on the same number of vertices is called the spectral distance of graphs. Some other spectral distances (also based on various graph matrices) have been considered as well. Two graphs are considered as similar if their spectral distance is small. If two graphs are at zero distance, they are cospectral. In this sense, cospectral graphs are similar. Other spectrally based measures of similarity between networks (not necessarily having the same number of vertices) have been used in Internet topology analysis, and in other areas. The notion of spectral distance enables the design of various meta-heuristic (e.g., tabu search, variable neighbourhood search) algorithms for constructing graphs with a given spectrum (spectral graph reconstruction). Several spectrally based pattern recognition problems appear in many areas (e.g., image segmentation in computer vision, alignment of protein-protein interaction networks in bio-informatics, recognizing hard instances for combinatorial optimization problems such as the travelling salesman problem). We give a survey of such and other graph spectral recognition techniques used in computer sciences.
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Wang, Yiyuan, Shaowei Cai, Shiwei Pan, Ximing Li, and Monghao Yin. "Reduction and Local Search for Weighted Graph Coloring Problem." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 2433–41. http://dx.doi.org/10.1609/aaai.v34i03.5624.

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The weighted graph coloring problem (WGCP) is an important extension of the graph coloring problem (GCP) with wide applications. Compared to GCP, where numerous methods have been developed and even massive graphs with millions of vertices can be solved well, fewer works have been done for WGCP, and no solution is available for solving WGCP for massive graphs. This paper explores techniques for solving WGCP, including a lower bound and a reduction rule based on clique sampling, and a local search algorithm based on two selection rules and a new variant of configuration checking. This results in our algorithm RedLS (Reduction plus Local Search). Experiments are conducted to compare RedLS with the state-of-the-art algorithms on massive graphs as well as conventional benchmarks studied in previous works. RedLS exhibits very good performance and robustness. It significantly outperforms previous algorithms on all benchmarks.
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Dissertations / Theses on the topic "Search in graphs AND"

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Habi, Abdelmalek. "Search and Aggregation in Big Graphs." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1259/document.

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Ces dernières années ont connu un regain d'intérêt pour l'utilisation des graphes comme moyen fiable de représentation et de modélisation des données, et ce, dans divers domaines de l'informatique. En particulier, pour les grandes masses de données, les graphes apparaissent comme une alternative prometteuse aux bases de données relationnelles. Plus particulièrement, le recherche de sous-graphes s'avère être une tâche cruciale pour explorer ces grands jeux de données. Dans cette thèse, nous étudions deux problématiques principales. Dans un premier temps, nous abordons le problème de la détection de motifs dans les grands graphes. Ce problème vise à rechercher les k-meilleures correspondances (top-k) d'un graphe motif dans un graphe de données. Pour cette problématique, nous introduisons un nouveau modèle de détection de motifs de graphe nommé la Simulation Relaxée de Graphe (RGS), qui permet d’identifier des correspondances de graphes avec un certain écart (structurel) et ainsi éviter le problème de réponse vide. Ensuite, nous formalisons et étudions le problème de la recherche des k-meilleures réponses suivant deux critères, la pertinence (la meilleure similarité entre le motif et les réponses) et la diversité (la dissimilarité entre les réponses). Nous considérons également le problème des k-meilleures correspondances diversifiées et nous proposons une fonction de diversification pour équilibrer la pertinence et la diversité. En outre, nous développons des algorithmes efficaces basés sur des stratégies d’optimisation en respectant le modèle proposé. Notre approche est efficiente en terme de temps d’exécution et flexible en terme d'applicabilité. L’analyse de la complexité des algorithmes et les expérimentations menées sur des jeux de données réelles montrent l’efficacité des approches proposées. Dans un second temps, nous abordons le problème de recherche agrégative dans des documents XML. Pour un arbre requête, l'objectif est de trouver des motifs correspondants dans un ou plusieurs documents XML et de les agréger dans un seul agrégat. Dans un premier temps nous présentons la motivation derrière ce paradigme de recherche agrégative et nous expliquons les gains potentiels par rapport aux méthodes classiques de requêtage. Ensuite nous proposons une nouvelle approche qui a pour but de construire, dans la mesure du possible, une réponse cohérente et plus complète en agrégeant plusieurs résultats provenant de plusieurs sources de données. Les expérimentations réalisées sur plusieurs ensembles de données réelles montrent l’efficacité de cette approche en termes de pertinence et de qualité de résultat<br>Recent years have witnessed a growing renewed interest in the use of graphs as a reliable means for representing and modeling data. Thereby, graphs enable to ensure efficiency in various fields of computer science, especially for massive data where graphs arise as a promising alternative to relational databases for big data modeling. In this regard, querying data graph proves to be a crucial task to explore the knowledge in these datasets. In this dissertation, we investigate two main problems. In the first part we address the problem of detecting patterns in larger graphs, called the top-k graph pattern matching problem. We introduce a new graph pattern matching model named Relaxed Graph Simulation (RGS), to identify significant matches and to avoid the empty-set answer problem. We formalize and study the top-k matching problem based on two classes of functions, relevance and diversity, for ranking the matches according to the RGS model. We also consider the diversified top-k matching problem, and we propose a diversification function to balance relevance and diversity. Moreover, we provide efficient algorithms based on optimization strategies to compute the top-k and the diversified top-k matches according to the proposed model. The proposed approach is optimal in terms of search time and flexible in terms of applicability. The analyze of the time complexity of the proposed algorithms and the extensive experiments on real-life datasets demonstrate both the effectiveness and the efficiency of these approaches. In the second part, we tackle the problem of graph querying using aggregated search paradigm. We consider this problem for particular types of graphs that are trees, and we deal with the query processing in XML documents. Firstly, we give the motivation behind the use of such a paradigm, and we explain the potential benefits compared to traditional querying approaches. Furthermore, we propose a new method for aggregated tree search, based on approximate tree matching algorithm on several tree fragments, that aims to build, the extent possible, a coherent and complete answer by combining several results. The proposed solutions are shown to be efficient in terms of relevance and quality on different real-life datasets
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Varadarajan, Ramakrishna R. "Ranked Search on Data Graphs." FIU Digital Commons, 2009. http://digitalcommons.fiu.edu/etd/220.

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Graph-structured databases are widely prevalent, and the problem of effective search and retrieval from such graphs has been receiving much attention recently. For example, the Web can be naturally viewed as a graph. Likewise, a relational database can be viewed as a graph where tuples are modeled as vertices connected via foreign-key relationships. Keyword search querying has emerged as one of the most effective paradigms for information discovery, especially over HTML documents in the World Wide Web. One of the key advantages of keyword search querying is its simplicity – users do not have to learn a complex query language, and can issue queries without any prior knowledge about the structure of the underlying data. The purpose of this dissertation was to develop techniques for user-friendly, high quality and efficient searching of graph structured databases. Several ranked search methods on data graphs have been studied in the recent years. Given a top-k keyword search query on a graph and some ranking criteria, a keyword proximity search finds the top-k answers where each answer is a substructure of the graph containing all query keywords, which illustrates the relationship between the keyword present in the graph. We applied keyword proximity search on the web and the page graph of web documents to find top-k answers that satisfy user’s information need and increase user satisfaction. Another effective ranking mechanism applied on data graphs is the authority flow based ranking mechanism. Given a top-k keyword search query on a graph, an authority-flow based search finds the top-k answers where each answer is a node in the graph ranked according to its relevance and importance to the query. We developed techniques that improved the authority flow based search on data graphs by creating a framework to explain and reformulate them taking in to consideration user preferences and feedback. We also applied the proposed graph search techniques for Information Discovery over biological databases. Our algorithms were experimentally evaluated for performance and quality. The quality of our method was compared to current approaches by using user surveys.
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Janmark, Jonatan. "Quantum Search on Strongly Regular Graphs." Thesis, KTH, Teoretisk fysik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-126266.

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Trippen, Gerhard Wolfgang. "Online exploration and search in graphs /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20TRIPPE.

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Korneffel, Torsten. "On combinatorial search problems which involve graphs." [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=984062386.

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Yuan, Wenjun, and 袁文俊. "Flexgraph: flexible subgraph search in large graphs." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B46087539.

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Jiang, Jiaxin. "Efficient frameworks for keyword search on massive graphs." HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/806.

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Due to the unstructuredness and the lack of schema information of knowledge graphs, social networks and RDF graphs, keyword search has been proposed for querying such graphs/networks. Recently, various keyword search semantics have been designed. However, these keyword search semantics and algorithms encounter efficiency or scalability issues. In this thesis, we propose new three generic frameworks or index techniques to address these issues. The thesis results show that the keyword search on massive graphs under different scenarios can be effective and efficient, which would facilitate keyword search services on graphs in the real world. First, we study the keyword search on massive knowledge graphs. In particular, we propose a generic ontology- based indexing framework for keyword search, called Bisimulation of Generalized Graph Index (BiG-index), to enhance the search performance. The novelties of BiG-index reside in using an ontology graph GOnt to summarize and index a data graph G iteratively, to form a hierarchical index structure G. Regarding query evaluation, we transform a keyword search q into Q according to GOnt in runtime. The transformed query is searched on the summary graphs in G. The efficiency is due to the small sizes of the summary graphs and the early pruning of semantically irrelevant subgraphs. To illustrate BiG-index's applicability, we show popular indexing techniques for keyword search can be easily implemented on top of BiG-index. Our extensive experiments show that BiG-index clearly reduced the runtimes of popular keyword search algorithms. Second, we study the problem of keyword search on public-private graph. In many applications (e.g., social networks), users may prefer to hide parts or all of her/his data graphs (e.g., private friendships) from the public. This leads to a recent graph model, namely the public-private network model, in which each user has his/her own network. While there have been studies on public-private network analysis, keyword search on public-private networks has not yet been studied. Hence, we propose a new keyword search framework, called public-private keyword search (PPKWS). PPKWS consists of three major steps: partial evaluation, answer refinement, and answer completion. We select three representative ones and show that they can be implemented on the model with minor modifications. We propose indexes and optimizations for PPKWS. We have verified through experiments that, on average, the algorithms implemented on top of PPKWS run 113 times faster than the original algorithms directly running on the public network attached to the private network for retrieving answers that span through them. Third, we study the keyword search in distributed graph evaluation systems. In the recent research on query evaluation, parallel evaluation has attracted much interest. However, the study on keyword search on distributed graphs has still been limited. We propose a novel distributed keyword search framework called DKWS. We propose a notify-push paradigm which can exchange the upper bounds of answers across all the workers asynchronously. In particular, the workers notify the coordinator when the local upper bound is refined. The coordinator pushes the refined global upper bound to all the workers. Moreover, we propose an efficient and generic keyword search algorithm for the workers. We have implemented DKWS on top of GRAPE, a distributed graph process system from our previous research collaboration. Extensive experimental results show that DKWS outperforms current-state-of-art techniques
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Hunter, Andrew. "Locality and Complexity in Path Search." Scholarship @ Claremont, 2009. https://scholarship.claremont.edu/hmc_theses/220.

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The path search problem considers a simple model of communication networks as channel graphs: directed acyclic graphs with a single source and sink. We consider each vertex to represent a switching point, and each edge a single communication line. Under a probabilistic model where each edge may independently be free (available for use) or blocked (already in use) with some constant probability, we seek to efficiently search the graph: examine (on average) as few edges as possible before determining if a path of free edges exists from source to sink. We consider the difficulty of searching various graphs under different search models, and examine the computational complexity of calculating the search cost of arbitrary graphs.
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Marshall, Oliver. "Search Engine Optimization and the connection with Knowledge Graphs." Thesis, Högskolan i Gävle, Företagsekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-35165.

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Aim: The aim of this study is to analyze the usage of Search Engine Optimization and Knowledge Graphs and the connection between them to achieve profitable business visibility and reach. Methods: Following a qualitative method together with an inductive approach, ten marketing professionals were interviewed via an online questionnaire. To conduct this study both primary and secondary data was utilized. Scientific theory together with empirical findings were linked and discussed in the analysis chapter. Findings: This study establishes current Search Engine Optimization utilization by businesses regarding common techniques and methods. We demonstrate their effectiveness on the Google Knowledge Graph, Google My Business and resulting positive business impact for increased visibility and reach. Difficulties remain in accurate tracking procedures to analyze quantifiable results. Contribution of the thesis: This study contributes to the literature of both Search Engine Optimization and Knowledge Graphs by providing a new perspective on how these subjects have been utilized in modern marketing. In addition, this study provides an understanding of the benefits of SEO utilization on Knowledge Graphs. Suggestions for further research: We suggest more extensive investigation on the elements and utilization of Knowledge Graphs; how the structure can be affected; which techniques are most effective on a bigger scale and how effectively the benefits can be measured. Key Words: Search Engine, Search Engine Optimization, SEO, Knowledge Graphs, Google My Business, Google Search Engine, Online Marketing.
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Chen, Yan. "Enhanced Web Search Engines with Query-Concept Bipartite Graphs." Digital Archive @ GSU, 2010. http://digitalarchive.gsu.edu/cs_diss/54.

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With rapid growth of information on the Web, Web search engines have gained great momentum for exploiting valuable Web resources. Although keywords-based Web search engines provide relevant search results in response to users’ queries, future enhancement is still needed. Three important issues include (1) search results can be diverse because ambiguous keywords in queries can be interpreted to different meanings; (2) indentifying keywords in long queries is difficult for search engines; and (3) generating query-specific Web page summaries is desirable for Web search results’ previews. Based on clickthrough data, this thesis proposes a query-concept bipartite graph for representing queries’ relations, and applies the queries’ relations to applications such as (1) personalized query suggestions, (2) long queries Web searches and (3) query-specific Web page summarization. Experimental results show that query-concept bipartite graphs are useful for performance improvement for the three applications.
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Books on the topic "Search in graphs AND"

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Extremal paths in graphs: Foundations, search strategies, and related topics. Akademie Verlag, 1997.

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Maon, Yael. Parallel ear decomposition search (EDS) and ST-numbering in graphs. Courant Institute of Mathematical Sciences, New York University, 1986.

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Aigner, Martin. Combinatorial search. B.G. Teubner, 1988.

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Fox, Gardner F. Justice League of America: Zatanna's search. DC Comics, 2004.

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der, Rol Ruud van, Schippers Lies, and Miller Lorraine T, eds. The search. Farrar, Straus and Giroux Books for Young Readers, 2009.

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Mahmoud, Hosam M. Evolution of random search trees. Wiley, 1992.

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Illinois. Dept. of Commerce and Economic Opportunity. eGrants Illinois: Search ... register ... apply ... track. Illinois Dept. of Commerce and Economic Opportunity, 2008.

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Doxiadēs, Apostolos K. Logicomix: An epic search for truth. Bloomsbury, 2009.

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1956-, Stinson Douglas R., ed. Combinatorial algorithms: Generation, enumeration, and search. CRC Press, 1999.

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Baltazar, Art. Itty Bitty Hellboy: The search for the were-jaguar! Dark Horse Comics, 2016.

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Book chapters on the topic "Search in graphs AND"

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Shamaeva, Irina, and David Galley. "Knowledge Graphs." In Custom Search – Discover more:. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003100133-16.

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Maier, Gunther. "Prerequisites: Graphs, Routes, and Computational Complexity." In Spatial Search. Physica-Verlag HD, 1995. http://dx.doi.org/10.1007/978-3-642-49346-1_3.

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Cournier, Alain. "Search in Indecomposable Graphs." In Graph-Theoretic Concepts in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36379-3_8.

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Jungnickel, Dieter. "Connectivity and Depth First Search." In Graphs, Networks and Algorithms. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32278-5_8.

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Moghtasedi, Shima. "Temporal Similarity of Trajectories in Graphs." In Similarity Search and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60936-8_32.

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Baeza-Yates, Ricardo. "Graphs from Search Engine Queries." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-69507-3_1.

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Portugal, Renato. "Quantum Walks on Infinite Graphs." In Quantum Walks and Search Algorithms. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6336-8_5.

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Portugal, Renato. "Quantum Walks on Finite Graphs." In Quantum Walks and Search Algorithms. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6336-8_6.

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Wang, Dong, Lei Zou, Wanqiong Pan, and Dongyan Zhao. "Keyword Graph: Answering Keyword Search over Large Graphs." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35527-1_53.

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Chalopin, Jérémie, Shantanu Das, and Peter Widmayer. "Deterministic Symmetric Rendezvous in Arbitrary Graphs: Overcoming Anonymity, Failures and Uncertainty." In Search Theory. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6825-7_12.

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Conference papers on the topic "Search in graphs AND"

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Krnc, Matjaž, and Nevena Pivač. "Graphs where Search Methods are Indistinguishable." In 7th Student Computer Science Research Conference. University of Maribor Press, 2021. http://dx.doi.org/10.18690/978-961-286-516-0.3.

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Graph searching is one of the simplest and most widely used tools in graph algorithms. Every graph search method is defined using some partic-ular selection rule, and the analysis of the corre-sponding vertex orderings can aid greatly in de-vising algorithms, writing proofs of correctness, or recognition of various graph families. We study graphs where the sets of vertex order-ings produced by two di˙erent search methods coincide. We characterise such graph families for ten pairs from the best-known set of graph searches: Breadth First Search (BFS), Depth First Search (DFS), Lexicographic Breadth First Search (LexBFS) and Lexicographic Depth First Search (LexDFS), and Maximal Neighborhood Search (MNS).
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Wang, Xuezhi, Roman Garnett, and Jeff Schneider. "Active search on graphs." In KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2013. http://dx.doi.org/10.1145/2487575.2487605.

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Sun, Bingjun, Prasenjit Mitra, and C. Lee Giles. "Learning to rank graphs for online similar graph search." In Proceeding of the 18th ACM conference. ACM Press, 2009. http://dx.doi.org/10.1145/1645953.1646252.

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Failla, Pierluigi. "Heuristic Search in Encrypted Graphs." In 2010 Fourth International Conference on Emerging Security Information, Systems and Technologies (SECURWARE). IEEE, 2010. http://dx.doi.org/10.1109/securware.2010.21.

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Li, Hua, and Edwin K. P. Chong. "Search on lines and graphs." In 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5399521.

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Agrawal, Rakesh, Sreenivas Gollapudi, Anitha Kannan, and Krishnaram Kenthapadi. "Similarity Search using Concept Graphs." In CIKM '14: 2014 ACM Conference on Information and Knowledge Management. ACM, 2014. http://dx.doi.org/10.1145/2661829.2661995.

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Liu, Ziyang, Chong Wang, and Yi Chen. "Keyword Search on Temporal Graphs." In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00261.

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Elbassuoni, Shady, and Roi Blanco. "Keyword search over RDF graphs." In the 20th ACM international conference. ACM Press, 2011. http://dx.doi.org/10.1145/2063576.2063615.

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Zhao, Tianxiang, Xiang Zhang, and Suhang Wang. "GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks." In WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining. ACM, 2021. http://dx.doi.org/10.1145/3437963.3441720.

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Zhang, Ziwei, Xin Wang, and Wenwu Zhu. "Automated Machine Learning on Graphs: A Survey." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/637.

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Machine learning on graphs has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks. To solve this critical challenge, automated machine learning (AutoML) on graphs which combines the strength of graph machine learning and AutoML together, is gaining attention from the research community. Therefore, we comprehensively survey AutoML on graphs in this paper, primarily focusing on hyper-parameter optimization (HPO) and neural architecture search (NAS) for graph machine learning. We further overview libraries related to automated graph machine learning and in-depth discuss AutoGL, the first dedicated open-source library for AutoML on graphs. In the end, we share our insights on future research directions for automated graph machine learning. This paper is the first systematic and comprehensive review of automated machine learning on graphs to the best of our knowledge.
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Reports on the topic "Search in graphs AND"

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Brost, Randolph C., Michelle Carroll, Debbie Dennison, et al. Geospatial-Temporal Semantic Graphs for Automated Wide-Area Search. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1527318.

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Wilson, Keith. Factoring Semiprimes Using PG2N Prime Graph Multiagent Search. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.219.

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Bekkerman, Ron, Shlomo Zilberstein, and James Allan. Web Page Clustering using Heuristic Search in the Web Graph. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada457111.

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Arlitsch, Kenning. Data set supporting the dissertation Semantic Web Identity in Academic Organizations: Search engine entity recognition and the sources that influence Knowledge Graph Cards in search results. Montana State University ScholarWorks, 2016. http://dx.doi.org/10.15788/m2f590.

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Schenian, Pamela A. A Cultural Resources Literature Search of the Bayou Du Chien Drainage Project Area in Fulton, Graves, and Hickman Counties, Kentucky. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada263119.

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Qi, Fei, Zhaohui Xia, Gaoyang Tang, et al. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.
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Powers, David. Eigenvectors of Graphs. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada203317.

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Powers, David L. Eigenvectors of Graphs. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada170562.

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Sanders, G., and R. Pearce. FINAL REPORT: Asynchronous Graphs. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1573144.

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Pisan, Yusuf. Visual Reasoning with Graphs. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada466292.

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