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Showing 1 to 14 of 14 for “"graph embeddings"”.

  1. Sufficient degree conditions for graph embeddings

    … on the sufficient conditions to guarantee one graph being the subgraph of another. In Chapter 2, we discuss list packing, a modification of the idea of graph packing. This is fitting one graph in the complement of another graph. Sauer and Spencer showed a sufficient bound involving maximum …

    uiuc Repository record for Sufficient degree conditions for graph embeddings (opens in a new tab)

  2. Graph embeddings for deep learning in general game playing

    … positions describing the game states but also a graph-based embedding of the game rules (derived from GDL). The game rules are encoded as either Rule Graphs or Propositional Networks, and then we experiment with several different graph-based embeddings for encoding the graphs. The result shows …

    reykjavik Repository record for Graph embeddings for deep learning in general game playing (opens in a new tab)

  3. Uncovering latent structure in social networks using graph embeddings

    … has been one of the commonly studied problems of graph mining, and is recognized as a challenging necessary task, and many open tasks are still poorly understood. We show that user information from social network platforms such as Instagram can be clustered using similarities based independently …

    queens Repository record for Uncovering latent structure in social networks using graph embeddings (opens in a new tab)

  4. Fact-based visual question answering using knowledge graph embeddings

    … It must include facts from a diverse knowledge graph (KG) in its reasoning process to produce an answer. Large KGs, especially common-sense KGs, are known to be incomplete, i.e., not all non-existent facts are always incorrect. Therefore, being able to reason over incomplete KGs for QA is a …

    uiuc Repository record for Fact-based visual question answering using knowledge graph embeddings (opens in a new tab)

  5. Plane Permutations and their Applications to Graph Embeddings and Genome Rearrangements

    … fields. A map is a 2-cell embedding of a graph on an orientable surface. Motivated by a new way to read the information provided by the skeleton of a map, we introduce new objects called plane permutations. Plane permutations not only provide new insight into enumeration of maps and …

    vt Repository record for Plane Permutations and their Applications to Graph Embeddings and Genome Rearrangements (opens in a new tab)

  6. Enumeration of polyhedral graphs

    … focuses on specic classes of polyhedra and their graph theoretic properties. This is then compared more broadly to other graph enumeration algorithms that are concerned with the same or a superset which includes these properties. An original and novel algorithm is contributed to this area. The …

    oxford-brookes Repository record for Enumeration of polyhedral graphs (opens in a new tab)

  7. Learning generalizable device placement algorithms for distributed machine learning

    … a device placement for a specific computation graph, Placeto can learn generalizable device placement policies that can be applied to any graph. We propose two key ideas in our approach: (1) we represent the policy as performing iterative placement improvements, rather than outputting a …

    mit Repository record for Learning generalizable device placement algorithms for distributed machine learning (opens in a new tab)

  8. Topological Operations for Genus Distributions and Embeddings of Graphs

    The research of graph embeddings on surfaces started from Euler's equation v e+f = 2. For a connected graph G, v, e and f represent the number of vertices, edges and regions of an embedding on a plane or sphere. Genus embedding of graphs is one of the most studied subjects in topological graph

    auckland-ms Repository record for Topological Operations for Genus Distributions and Embeddings of Graphs (opens in a new tab)

  9. Drug Repurposing Using Gene Expression Data Mining

    … <p>Third, I have applied the knowledge graph model to drug repurposing. The model integrates multiple sources of information from diverse biomedical databases, including genes, drugs, phenotypes, and patients. The knowledge graph embeddings provide representations of biological entities …

    cuny-grad Repository record for Drug Repurposing Using Gene Expression Data Mining (opens in a new tab)

  10. Graph-Based Machine Learning for Passive Network Reconnaissance within Encrypted Networks

    … conditions. In contrast, we devise a bipartite graph-based representation to create network reconnaissance solutions that rely only on a single feature (e.g., the Internet protocol (IP) address field). We exploit a widely available feature set to provide network reconnaissance solutions that are …

    adelaide Repository record for Graph-Based Machine Learning for Passive Network Reconnaissance within Encrypted Networks (opens in a new tab)

  11. A Framework for Semantic Enterprise Transformations

    … and applies these patterns to an underlying graph structure. By generalizing semantic transformations through patterns and graphs (Chapter 5), it is possible not only to determine a traversal path to find a corresponding code value, but to predict missing nodes through the use of graph

    calgary Repository record for A Framework for Semantic Enterprise Transformations (opens in a new tab)

  12. Genus Distributions of Graphs Constructed Through Amalgamations

    Graphs are commonly represented as points in space connected by lines. The points in space are the vertices of the graph, and the lines joining them are the edges of the graph. A general definition of a graph is considered here, where multiple edges are allowed between two vertices and an edge is …

    columbia-diss Repository record for Genus Distributions of Graphs Constructed Through Amalgamations (opens in a new tab)

  13. Topics in trivalent graphs

    … most important property of E/Z* is the Unique graph theorem: unlike in E, a list of which reduced vectors are edges uniquely determines graph structure (if edge connectivity is high enough; that covers certain “solid” components every trivalent graph can be decomposed into). Chapter 2 gives a …

    birmingham Repository record for Topics in trivalent graphs (opens in a new tab)

  14. The resurgence of structure in deep neural networks

    … (operating on sparse multimodal and graph-structured data), and a structure-informed learning algorithm for graph neural networks, demonstrating significant outperformance of conventional baseline models and algorithms.

    cambridge Repository record for The resurgence of structure in deep neural networks (opens in a new tab)