Abstract: A computer-implemented method includes: in response to receiving a query, retrieving a plurality of documents; generating a first graph representation which graphically represents the plurality of documents utilizing a plurality of nodes indicating a concept and a plurality of edges indicating a relationship between at least two nodes among the plurality of nodes; generating a second graph representation with respect to the plurality of documents, based on connection information associated with the first graph representation; ranking the plurality of documents, based on the second graph representation; and applying one or more machine-learned models to generate a response to the query based on the plurality of documents ranked based on the second graph representation.

Abstract: A computer-implemented method includes: in response to receiving a query, retrieving a plurality of documents; generating a first graph representation which graphically represents the plurality of documents utilizing a plurality of nodes indicating a concept and a plurality of edges indicating a relationship between at least two nodes among the plurality of nodes; generating a second graph representation with respect to the plurality of documents, based on connection information associated with the first graph representation; ranking the plurality of documents, based on the second graph representation; and applying one or more machine-learned models to generate a response to the query based on the plurality of documents ranked based on the second graph representation.

Abstract: A computer-implemented method includes: in response to receiving a query, retrieving a plurality of documents; generating a first graph representation which graphically represents the plurality of documents utilizing a plurality of nodes indicating a concept and a plurality of edges indicating a relationship between at least two nodes among the plurality of nodes; generating a second graph representation with respect to the plurality of documents, based on connection information associated with the first graph representation; ranking the plurality of documents, based on the second graph representation; and applying one or more machine-learned models to generate a response to the query based on the plurality of documents ranked based on the second graph representation.

Abstract: A distributed computing system is configured to perform operations for embedding graphs of large scale. The system can generate node sequences from a target graph, determine training samples, and perform unsupervised learning using counts of co-occurrences between nodes to iteratively update an embedding table and learn a low-dimensional representation of the graph.

Abstract: Systems and methods for graph model search and/or for architecture insight can include training and testing a plurality of graph models. For example, the systems and methods can generate a plurality of synthetic graph datasets, which can then be utilized to train a plurality of graph models with varying graph model architectures. The trained graph models can then be evaluated based on outputs generated by the models based on test inputs. The evaluation data can then be utilized for providing particular graph model insight and/or may be utilized to enable task-specific graph model search.

Abstract: Systems and methods for generating single-node representations in graphs comprised of linked nodes. The present technology enables generation of individual node embeddings on the fly in sublinear time (less than O(n), where n is the number of nodes in graph G) using only a PPR vector for the node, and random projection to reduce the dimensionality of the node’s PPR vector. In one example, the present technology includes a computer-implemented method comprising obtaining a graph having a plurality of nodes from a database, generating a personal pagerank vector for a given node of the plurality of nodes, and producing an embedding vector for the given node by randomly projecting the personal pagerank vector, wherein the embedding vector has lower dimensionality than the personal pagerank vector.