Abstract: A method for improving computing efficiency of a computing device for temporal directed cycle detection in a transaction graph includes preparing the transaction graph based on a plurality of transactions, the transaction graph including nodes indicating transaction origination points and transaction destination points, and edges indicating interactions between the nodes. Irrelevant nodes in the transaction graph are identified and pruned to provide a pruned, preprocessed transaction graph which can be partitioning into sections, where each section includes selected nodes that are linked to other linked nodes therein. Each of the sections having non-cyclic nodes can be trimmed prior to performing cycle detection on the resulting pruned transaction graph. Postprocessing pruning can be performed to further reduce the number of detected cycles that may be of interest to a particular application, such as in anti-money laundering.

Abstract: A system may receive a dynamic network graph and select an origination node. From the origination node, the system may deploy a random walk simulation on the dynamic network graph simulating steps from the origination node to one or more other nodes, and determine a convergence node for the random walk simulation.

Abstract: An example operation may include one or more of identifying a digital token issued via a blockchain, determining an asset that is linked to the digital token, a current custodian of the asset that is linked to the digital token, and a risk value associated with the asset based on attributes of the asset, generating a graph that comprises a path between a user that owns the digital token and the current custodian of the asset, embedding the graph within a field of a template and embedding the risk value within a second field of the template to generate a filled-in template, and displaying the filled-in template within a user interface.

Abstract: A system includes a plurality of graph convolutional networks corresponding to a plurality of time steps, each network modelling a graph including nodes and edges, and in turn including a plurality of graph convolution units; an evolving mechanism; and an output layer. Each of the units, for a given one of the time steps, takes as input a graph adjacency matrix, a node feature matrix, and a parameter matrix for a current layer, and outputs a new node feature matrix for a next highest layer. The mechanism takes as input a parameter matrix for a prior time step updates the input parameter matrix, and outputs the parameter matrix for the given time step. The output layer obtains, as input, output of each of the units for a final time step, and based on the output of each of the units for the final time step, outputs a graph solution.

Abstract: A dynamic graph embedding method for transaction data analysis includes obtaining transaction data associated with an account during a plurality of time windows, extracting spatial-temporal information of the transaction data by using a graph convolutional network and a transformer framework, and generating a feature representation for the account based on the spatial-temporal information.

Abstract: A novel distributed graph database is provided that is designed for efficient graph data storage and processing on modern computing architectures. In particular a single node graph database and a runtime & communication layer allows for composing a distributed graph database from multiple single node instances.

Abstract: A suspicious pattern can be detected within a transaction network of nodes. Nodes of the network are walked by determining if any adjacent node to a current node is within the suspicious pattern. If an adjacent node is within the suspicious pattern, that node is walked to. Based on the walk, a node sequence can be generated.

Abstract: A novel distributed graph database is provided that is designed for efficient graph data storage and processing on modern computing architectures. In particular a single node graph database and a runtime & communication layer allows for composing a distributed graph database from multiple single node instances.

Abstract: A novel distributed graph database is provided that is designed for efficient graph data storage and processing on modern computing architectures. In particular a single node graph database and a runtime & communication layer allows for composing a distributed graph database from multiple single node instances.

Abstract: A fine-grained scalable time-versioning support for large-scale property graph databases includes receiving a request, wherein the request includes an entity identifier (ID) and reference time information, and searching entries of a first table using the entity ID as a first key. Matching an entry is performed corresponding to the first key in the first table, determining the entry is valid based at least in part on comparing the reference time information of the request with stored time information for the entry, and responsive to the determination, creating a second key, wherein the second key concatenates an ID associated with the entity ID with the reference time information of the request. Searching a second table is performed having entries based at least in part on the second key, and responsive to searching the second table, retrieving data associated with the second key.

Abstract: A generation apparatus that generates a mapping between individual properties included in an object in a program and individual elements of a structured document. The generation apparatus includes: an object tree generation unit that generates a tree structure representing hierarchical structure of the object by assigning the individual properties included in the object to nodes of the tree structure; and a selection unit that selects a mapping minimizing conversion cost of converting the tree structure of the object to a tree structure that includes the individual elements of the structured document as its nodes. The selection is from mappings that associate the individual properties included in the object with the individual elements of the structured document.

Abstract: A generation apparatus that generates a mapping between individual properties included in an object in a program and individual elements of a structured document. The generation apparatus includes: an object tree generation unit that generates a tree structure representing hierarchical structure of the object by assigning the individual properties included in the object to nodes of the tree structure; and a selection unit that selects a mapping minimizing conversion cost of converting the tree structure of the object to a tree structure that includes the individual elements of the structured document as its nodes. The selection is from mappings that associate the individual properties included in the object with the individual elements of the structured document.

Abstract: A method for improving computing efficiency of a computing device for temporal directed cycle detection in a transaction graph includes preparing the transaction graph based on a plurality of transactions, the transaction graph including nodes indicating transaction origination points and transaction destination points, and edges indicating interactions between the nodes. Irrelevant nodes in the transaction graph are identified and pruned to provide a pruned, preprocessed transaction graph which can be partitioning into sections, where each section includes selected nodes that are linked to other linked nodes therein. Each of the sections having non-cyclic nodes can be trimmed prior to performing cycle detection on the resulting pruned transaction graph. Postprocessing pruning can be performed to further reduce the number of detected cycles that may be of interest to a particular application, such as in anti-money laundering.

Abstract: A system includes a plurality of graph convolutional networks corresponding to a plurality of time steps, each network modelling a graph including nodes and edges, and in turn including a plurality of graph convolution units; an evolving mechanism; and an output layer. Each of the units, for a given one of the time steps, takes as input a graph adjacency matrix, a node feature matrix, and a parameter matrix for a current layer, and outputs a new node feature matrix for a next highest layer. The mechanism takes as input a parameter matrix for a prior time step updates the input parameter matrix, and outputs the parameter matrix for the given time step. The output layer obtains, as input, output of each of the units for a final time step, and based on the output of each of the units for the final time step, outputs a graph solution.

Abstract: A Resilient Distributed Dataset (RDD) processing method, system, and computer program product include sending a differential portion of a message from a receiver to a sender along with information of a global identifier of an RDD and differential values after matching incoming message.

Abstract: A Resilient Distributed Dataset (RDD) processing method, system, and computer program product include sending a different portion including a global identifier and different values of a message from a receiver to a sender and at a receiver side, an already-processed message is extracted from an RDD object repository corresponding to the global identifier of the different portion and the incoming differential portion is applied to the already-processed message.

Abstract: A remote control system detects a directional signal at an alerting sensor package; activates an appliance to accept a control signal in response to the directional signal; accepts the control signal at the appliance from a muscle sensor package; and adjusts operation of the appliance in response to the control signal.

Abstract: Techniques facilitating generation of a fused kernel that can approximate a full kernel of a convolutional neural network are provided. In one example, a computer-implemented method comprises determining a first pattern of samples of a first sample matrix and a second pattern of samples of a second sample matrix. The first sample matrix can be representative of a sparse kernel, and the second sample matrix can be representative of a complementary kernel. The first pattern and second pattern can be complementary to one another. The computer-implemented method also comprises generating a fused kernel based on a combination of features of the sparse kernel and features of the complementary kernel that are combined according to a fusing approach and training the fused kernel.

Abstract: Methods and systems for estimating a probability of re-sharing information include extracting keywords from a set of documents addressed to a user. The keywords from the set of documents are weighted according a metric for the user's interest in the keywords' respective source documents to create an interest model. A new document having one or more keywords is received. A likelihood that the user will re-share the new document is determined. The likelihood is based on the interest model and the one or more keywords present in the new document. The new document is automatically responded to based on the determined likelihood.

Abstract: A novel distributed graph database is provided that is designed for efficient graph data storage and processing on modern computing architectures. In particular a single node graph database and a runtime & communication layer allows for composing a distributed graph database from multiple single node instances.