Abstract: A search system facilitates efficient and fast near neighbor search given item vector representations of items, regardless of item type or corpus size. To index an item, the search system expands an item vector for the item to generate an expanded item vector and selects elements of the expanded item vector. The item is index by storing an identifier of the item in posting lists of an index corresponding to the position of each selected element in the expanded item vector. When a query is received, a query vector for the item is expanded to generate an expanded query vector, and elements of the expanded query vector are selected. Candidate items are identified based on posting lists corresponding to the position of each selected element in the expand query vector. The candidate items may be ranked, and a result set is returned as a response to the query.

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for generating generate explanatory paths for column annotations determined using a knowledge graph and a deep representation learning model. For instance, the disclosed systems can utilize a knowledge graph to generate an explanatory path for a column label determination from a deep representation learning model. For example, the disclosed systems can identify a column and determine a label for the column using a knowledge graph (e.g., a representation of a knowledge graph) that includes encodings of columns, column features, relational edges, and candidate labels. Then, the disclosed systems can determine a set of candidate paths between the column and the determined label for the column within the knowledge graph. Moreover, the disclosed systems can generate an explanatory path by ranking and selecting paths from the set of candidate paths using a greedy ranking and/or diversified ranking approach.

Abstract: A method of determining efficacy of a dataset includes receiving data from a data source, wherein the data comprises a plurality of fields of unknown efficacy; mapping the data based on a plurality of data quality metrics and based on attributes of the plurality of fields wherein meta-features for the data are obtained; predicting a value for each of the plurality of data quality metrics using a ML model that takes the meta-features as input, wherein the value indicates whether a corresponding data quality metric is suitable for measuring efficacy of the fields; selecting a data quality metric based on the value, wherein the data quality metric measures an efficacy of the fields; and monitoring the efficacy of the fields in the data received from the data source based on the data quality metric.

Abstract: This disclosure describes one or more embodiments of systems, non-transitory computer-readable media, and methods that intelligently and automatically analyze input data and generate visual data stories depicting graphical visualizations from data insights determined from the input data. For example, the disclosed systems automatically extract data insights utilizing an in-depth statistical analysis of dataset groups from data-attribute categories within the input data. Based on the data insights, the disclosed systems can automatically generate exportable visual data stories to visualize the data insights, provide textual or audio-based natural language summaries of the data insights, and animate such data insights in videos. In some embodiments, the disclosed systems generate a visual-data-story graph comprising nodes representing visual data stories and edges representing similarities between the visual data stories.

Abstract: In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network.

Abstract: Systems and methods for training a neural network are described. One or more embodiments of the present disclosure include training a neural network based on a first combined gradient of a loss function at a plurality of sampled elements of a dataset; receiving an insertion request that indicates an insertion element to be added to the dataset, or a deletion request that indicates a deletion element to be removed from the dataset, wherein the deletion element is one of the plurality of sampled elements; computing a second combined gradient of the loss function by adding the insertion element to the dataset or by replacing the deletion element with a replacement element from the dataset; determining whether the first combined gradient and the second combined gradient satisfy a stochastic condition; and retraining the neural network to obtain a modified neural network based on the determination.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing hash partitions to determine local densities and distances among users (or among other represented data points) for clustering sparse data into segments. For instance, the disclosed systems can generate hash signatures for users in a sparse dataset and can map users to hash partitions based on the hash signatures. The disclosed systems can further determine local densities and separation distances for particular users (or other represented data points) within the hash partitions. Upon determining local densities and separation distances for datapoints from the dataset, the disclosed systems can select a segment (or cluster of data points) grouped according to a hierarchy of a clustering algorithm, such as a density-peaks-clustering algorithm.

Abstract: Embodiments of the present invention provide a system for querying a graph based on applying filters to a visual representation of the graph. The system allows complicated graph query operations to be performed with ease visually. During operation, the system obtains data indicating vertices and edges of a graph. The system displays a visual representation of the graph for a user. The system receives, from the user, a command defining a local graph filter comprising a region in the visual representation. The system then filters a representation of the graph, and stores the filtered representation.

Abstract: Deriving network embeddings that represent attributes of, and relationships between, different nodes in a network while preserving network data temporal and structural properties is described. A network representation system generates a plurality of graph time-series representations of network data that each includes a subset of nodes and edges included in a time segment of the network data, constrained either by time or a number of edges included in the representation. A temporal graph of the network data is generated by implementing a temporal model that incorporates temporal dependencies into the graph time-series representations. From the temporal graph, network embeddings for the network data are derived, where the network embeddings capture temporal dependencies between nodes, as indicated by connecting edges, as well as temporal structural properties of the network data.

Abstract: In implementations of systems for generating interactive reports, a computing device implements a report system to receive input data describing a dataset and an analytics report for the dataset that depicts a result of performing analytics on the dataset. The report system generates a declarative specification that describes the analytics report in a language that encodes data as properties of graphic objects. Editing data is received describing a user input specifying a modification to the analytics report. The report system modifies the declarative specification using the language that encodes data as properties of graphic objects based on the user input and the dataset. An interactive report is generated based on the modified declarative specification that includes the analytics report having the modification.

Abstract: In implementations of multi-item influence maximization, a computing device can obtain updates to a user association graph that indicates social correspondence between users, and obtain updates to a user-item graph that indicates user correspondence with one or more items. The computing device includes an influence maximization module that can update an item association graph that indicates item correspondence of each item with one or more other items, where the item association graph can be updated based on the user-item graph that indicates the user correspondence with one or more of the items. The influence maximization module can then iteratively determine a resource allocation for each of the users to maximize user influence of multiple items that are associated in the item association graph and based on the social correspondence between the users, as well as assign a variable portion of the resource allocation to any number of the users.

Abstract: In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a dynamic user interface and machine learning tools to generate data-driven digital content and multivariate testing recommendations for distributing digital content across computer networks. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to generate digital recommendations at multiple development stages of digital communications that are targeted on particular performance metrics. For example, the disclosed systems utilize historical information and recipient profile data to generate recommendations for digital communication templates, fragment variants of content fragments, and content variants of digital content items.

Abstract: This disclosure describes one or more embodiments of systems, non-transitory computer-readable media, and methods that intelligently and automatically analyze input data and generate visual data stories depicting graphical visualizations from data insights determined from the input data. For example, the disclosed systems automatically extract data insights utilizing an in-depth statistical analysis of dataset groups from data-attribute categories within the input data. Based on the data insights, the disclosed systems can automatically generate exportable visual data stories to visualize the data insights, provide textual or audio-based natural language summaries of the data insights, and animate such data insights in videos. In some embodiments, the disclosed systems generate a visual-data-story graph comprising nodes representing visual data stories and edges representing similarities between the visual data stories.

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for dynamically determining schema labels for columns regardless of information availability within the columns. For example, the disclosed systems can identify a column that contains an arbitrary amount of information (e.g., a header-only column, a cell-only column, or a whole column). Additionally, the disclosed systems can generate a vector embedding for an arbitrary input column by selectively using a header neural network and/or a cell neural network based on whether the column includes a header label and/or whether the column includes a populated column cell. Furthermore, the disclosed systems can compare the column vector embedding to schema vector embeddings of candidate schema labels in a d-dimensional space to determine a schema label for the column.

Abstract: Embodiments of the present invention provide a system for querying a graph based on applying filters to a visual representation of the graph. The system allows complicated graph query operations to be performed with ease visually. During operation, the system obtains data indicating vertices and edges of a graph. The system displays a visual representation of the graph for a user. The system receives, from the user, a command defining a local graph filter comprising a region in the visual representation. The system then filters a representation of the graph, and stores the filtered representation.

Abstract: Techniques and systems are described for analytics system entity resolution. Typed higher-order node combinations are determined within a dataset, and an amount of similarity between two arbitrary nodes within the dataset is determined based on the typed higher-order node combinations. The amount of similarity enables the digital analytics to accurately perform source resolution of portions of the dataset to a respective source, and may be utilized to control output of digital content to a client device.

Abstract: Various embodiments describe techniques for making inferences from graph-structured data using graph convolutional networks (GCNs). The GCNs use various pre-defined motifs to filter and select adjacent nodes for graph convolution at individual nodes, rather than merely using edge-defined immediate-neighbor adjacency for information integration at each node. In certain embodiments, the graph convolutional networks use attention mechanisms to select a motif from multiple motifs and select a step size for each respective node in a graph, in order to capture information from the most relevant neighborhood of the respective node.

Abstract: A system receives a temporal graph comprising nodes having respective identifiers and edges. Each of the edges has a direction pointing from a first node to a second node and indicates an association of the first node with the second node. The system generates a sequence of nodes and a sequence of edges by traversing the temporal graph. The system determines, for each node of the sequence of nodes, a respective set of feature values including an indegree, an outdegree, and a total degree. The system determines, for each edge of the sequence of edges, an edge feature comprising a sum of the total degree of a first node preceding the edge and the total degree of a second node following the edge. The system forms an edge feature values sequence for the sequence of edges and determines an edge network embedding for each edge of the sequence of edges.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data visualization generation. In one implementation, dataset intent data, visual design intent data, and insight intent data determined from a user input natural language query are obtained. A set of candidate intent recommendations is generated using various combinations of the dataset intent data, visual design intent data, and insight intent data. Each of the candidate intent recommendations is incorporated into a set of visualization templates to determine eligibility of the candidate intent recommendations. For eligible candidate intent recommendations, a score associated with a corresponding visualization template is determined. Based on the scores, a candidate intent recommendation and corresponding visualizations template is selected to use as a visual recommendation for presenting a data visualization.