18 results about "Adjacency list" patented technology

The invention belongs to the field of complex document processing, and provides a complex document-oriented dynamic DAG task decomposition and scheduling method and system in order to solve the problem of low complex document generation efficiency. The complex document-oriented dynamic DAG task decomposition and scheduling method comprises the following steps of: constructing an initial directed acyclic graph to form an initial task chain; receiving a node insertion request, calculating a dependency probability between any two nodes in all the non-ring adjacency lists by using a graph neural network model, and determining whether to add an implicit dependency edge according to the dependency probability so as to form an implicit dependency edge set; calculating task resource requirements of a to-be-generated document according to the video memory prediction model, dynamically allocating hardware resources and executing corresponding tasks in combination with node task types and resource requirement labels of all implicit dependency edges in the implicit dependency edge set, and aggregating execution results of all the tasks to generate a final document. The reliability and efficiency of a large model in a complex document generation task can be effectively improved.

The application provides a graph database query method and device, electronic equipment and a storage medium. The method comprises the following steps: according to a query condition, querying target edges corresponding to a starting vertex and attribute information of the target edges in a graph database; generating an adjacency list of the starting vertex according to the target edges and the attribute information; and querying a path in the graph database based on the adjacency list. By querying the target edges corresponding to the starting vertex in the graph database, the adjacency list is constructed based on the target edges and the attribute information. The adjacency list comprises path relationships between vertices. The path in the graph database is queried based on the adjacency list, and a required path is found, thereby improving the situation that the query operation fails when directly querying the graph database, and improving the accuracy of the query result.

The invention provides a method and a device for constructing a distributed approximate neighbor search graph index. The method comprises the following steps of: initializing each process, dividing batches for a read-only vector data set, and distributing a message buffer area; if the value of the counter is an even number, unprocessed data set batches are taken out, an adjacency list of each point is calculated in parallel, and a tuple is generated; if the counter value is an odd number, determining the affected points in the graph index updating process, and performing parallel pruning again and updating; the adjacency list length information is written into an adjacency list length array in parallel in the process; copying the information to a message sending buffer area in parallel by utilizing a parallel prefix and a calculation offset; broadcasting a sending message buffer area of each process; deserializing a tuple from the received message buffer area by each process, and updating a graph index in the process; generating tuples for the adjacency table of the updated points; printing related results by one process and outputting a graph index. According to the method, the index construction time is greatly shortened; and higher query accuracy can be provided in a query stage.

This application relates to the fields of power distribution network technology and deep learning technology. It discloses a method, apparatus, and device for generating power distribution network topology diagrams based on temporally explicit networks. The method includes: a power distribution network automation system generating an adjacency list for the current time; dividing multiple nodes in the adjacency list into multiple regions, selecting the bus node of each region as the traversal start node for each region, creating a corresponding worker thread for each region, and performing a breadth-first search traversal operation on each region starting from the traversal start node to obtain a connection tree for each region; merging the connection trees of each region to generate the connection tree of the power distribution network; converting the connection tree of the power distribution network into a topology matrix of the power distribution network; inputting the topology matrix of the power distribution network into a drawing tool; and having the drawing tool generate the topology diagram of the power distribution network at the current time. This application is beneficial for improving the efficiency of topology diagram generation.

The invention relates to the technical field of power distribution networks and the technical field of deep learning, and discloses a power distribution network topological graph generation method, device and equipment based on a time clear network, and the method comprises the steps: a power distribution network automation system generates an adjacency list at a current moment; dividing a plurality of nodes in the adjacency list into a plurality of areas, selecting a bus node of each area as a traversal starting node of each area, creating a working thread corresponding to each area, and starting from the traversal starting node in each area through the working thread corresponding to each area; performing a traversal operation of breadth-first search on each region to obtain a connection tree of each region, merging the connection trees of each region to generate a connection tree of the power distribution network, converting the connection tree of the power distribution network into a topological matrix of the power distribution network, inputting the topological matrix of the power distribution network into a drawing tool, and drawing the topological matrix of the power distribution network. The drawing tool generates a topological graph of the power distribution network at the current moment. The generation efficiency of the topological graph can be improved.

The invention discloses a workflow automatic layout method based on topological sorting and dynamic adjustment, which comprises the following steps of: analyzing input data, constructing a workflow directed graph of modules and a connection relationship thereof, constructing an adjacency list and an in-degree list based on the workflow directed graph, and then hierarchically dividing the modules by using an improved topological sorting algorithm, and dynamically calculating the coordinate position of each module in the canvas in combination with canvas boundary constraint and minimum spacing limitation, finally outputting JSON data containing complete layout information of each module, and realizing flow chart drawing in combination with a front-end visualization technology. According to the scheme, the problems of low manual layout efficiency, node omission, layout chaos and the like caused by loop dependence in a traditional layout algorithm are effectively solved, and the method has a good visualization effect and high adaptability and can be applied to automatic layout and scene generation of the flow chart of modular systems such as a software system, a micro-service architecture and a low-code platform.

The invention discloses a ciphertext graph K-hop reachability rapid query method supporting privacy protection, and aims to solve the limitation in a traditional spanning tree K-hop query scheme, including that nodes only belong to a small number of spanning trees, a large number of node pairs need to be realized through cross-tree query, non-tree edges are recursively traversed by depending on an adjacency list, and the efficiency is very low; and no clear path starting point guidance exists in root node selection, so that the coverage range of the spanning tree is dispersed, and the maximum coverage capability is insufficient. And a high-coverage, high-security and high-efficiency ciphertext graph K-hop reachability rapid query method supporting privacy protection is designed. The multi-round root node BFS spanning tree and circular pruning collaborative method is provided, the efficiency bottleneck of frequent recursion operation during cross-spanning tree K-hop query of a traditional graph is solved, and an accessibility result can be quickly researched and judged through intersection operation of spanning tree sets where starting and ending points are located; besides, a node-spanning tree and spanning tree-node triple efficient ciphertext index architecture is constructed by fusing cryptographic technologies such as homomorphic encryption and order-preserving encryption, so that double breakthrough of efficiency and security of K-hop reachability query of the ciphertext DAG is realized, and a new technical approach and solution are provided for privacy-protected graph data processing.

The invention relates to the technical field of graph data processing, and provides a graph data processing method based on cache optimization, which comprises the following steps: constructing a cache optimization representation of graph data, and distributing a bit vector GT-vector with a fixed length of k for each vertex in a graph, each bit representing whether the vertex belongs to a pre-calculated maximum independent set; reserving adjacency list representation of the graph data, and storing the adjacency list representation as reference data in a memory; for an edge query request, reading GT-vectors of two vertexes from a CPU cache, and executing bit and operation; if the bit and the result are non-zero, judging that the edge is non-edge and immediately returning a first Boolean result indicating that the edge does not exist; and if the bit and result is zero, querying the adjacency list in the memory for verification, and returning a second Boolean result indicating that the edge exists or does not exist. According to the method and the device, the hybrid architecture combining the bit vector representation of cache optimization and the adjacency list is constructed, so that the magnitude order improvement of the graph data processing performance is realized on the premise of ensuring that the query result is completely accurate.

The application relates to a dense subgraph-based graph compression method and system, and belongs to the technical field of computer software. The application reorders and reencodes nodes by using dense subgraphs, so that node ordering is not performed on a large graph, time for node ordering is reduced, and redundancy consumption of each node in the memory is reduced; each node in a K-edge-connected subgraph is re-encoded, a new edge storage representation is constructed, and space occupied by nodes with high occurrence frequencies in the graph is reduced; for a sparse space of the graph, a compressed adjacency list is used to reduce storage consumption, and a two-level index is used to improve the search efficiency of node neighbors. The application is simple and easy to use, reduces the gap distance between nodes, and effectively reduces the space required for storing the graph in the memory.

This invention discloses a maximal biclique enumeration method based on hybrid storage. It obtains a bipartite graph G(U,V,E), where U and V are two disjoint vertex sets and E is an edge set. A threshold t is set to determine the storage method of the subgraph during runtime. If the number of vertices in U is greater than t, G0 is stored using an adjacency list; otherwise, G0 is stored using a bitmap. The BicliqueFind(L,R,C,G) function is recursively called with U, V, and G0 as initial parameters. The count results of all maximal bicliques are output to the user. This invention employs a hybrid storage method, retaining the low storage overhead of adjacency list storage while combining the computational efficiency of bitmap storage. For tasks in biclique enumeration that require calculations only on small graphs, this invention significantly improves the execution speed of the biclique enumeration task by dynamically constructing sub-bitmaps.

This invention discloses a method for local feature transfer and fusion of a 3D model, belonging to the field of 3D reconstruction technology. The method includes: loading and parsing the original model and the target model; constructing and caching a mesh topology adjacency list to achieve O(1) complexity neighbor lookup; selecting the corresponding boundary control circle and performing centroid normalization; calculating the optimal rotation matrix through singular value decomposition of the covariance matrix, and constructing a rigid transformation matrix by combining an adaptive scaling factor; replacing the vertices of the local region of the transformed target model with those of the original model to generate a fused model; performing multi-level expansion of the splicing region based on the adjacency list, and applying Taubin forward and reverse smoothing iterations to eliminate seams. This invention ensures registration accuracy and global optimality through SVD decomposition, effectively prevents model volume shrinkage through Taubin bidirectional smoothing, and significantly improves computational efficiency through the adjacency list caching mechanism, achieving high-quality, high-efficiency, and automated local feature transfer and fusion of a 3D model.

The application discloses a local differential privacy graph data synthesis algorithm based on a neighborhood structure, and comprises the following steps: obtaining the disturbed node degree and the adjacency list of each user, and calculating a projection threshold value for constraining the sensitivity of triangle counting; using the projection threshold value and the disturbance mechanism to guide the user to calculate and report the core neighborhood structure information, and jointly optimizing the collected two neighborhood statistics of the degree and the triangle count value, correcting the structural inconsistency, and completing the generation of the target statistics; using the corrected target statistics to group all nodes, and sequentially executing an edge generation algorithm within and between groups, preferentially meeting the triangle count target, and then meeting the degree value requirement, and completing the construction of the synthesized graph meeting the local differential privacy protection. Under strict privacy protection, the application significantly improves the availability and fidelity of the synthesized graph data by maintaining the key neighborhood structure of the graph.

The invention discloses a local differential privacy graph data synthesis algorithm based on a neighborhood structure, and the algorithm comprises the steps: obtaining a node degree and an adjacency list after disturbance of each user, and calculating a projection threshold value used for constraining the triangulation counting sensitivity; guiding a user to calculate and report core neighborhood structure information by using a projection threshold value and a disturbance mechanism, and carrying out joint optimization on two neighborhood statistics, namely a collected degree and a triangular count value, correcting the structure inconsistency, and completing the generation of a target statistic; and grouping all nodes by using the corrected target statistics, and executing an edge generation algorithm in the groups and among the groups in sequence, preferably meeting a triangular counting target, and then meeting a degree value requirement, thereby completing construction of the composite graph meeting the local differential privacy protection. According to the method, under strict privacy protection, the availability and fidelity of the composite graph data are remarkably improved by keeping the key neighborhood structure of the graph.

The invention discloses a three-dimensional model local feature transfer fusion processing method, and belongs to the technical field of three-dimensional reconstruction. The method comprises the following steps: loading and analyzing an original model and a target model; constructing and caching a grid topology adjacency list to realize neighbor query with O (1) complexity; selecting a corresponding boundary control circle and carrying out centroid normalization; calculating an optimal rotation matrix through singular value decomposition of a covariance matrix, and constructing a rigid transformation matrix in combination with an adaptive scaling factor; replacing the vertexes of the local region of the transformed target model to the original model to generate a fusion model; and carrying out multi-stage expansion on the spliced area based on the adjacency list, and eliminating seams by applying Taubin forward and reverse smooth iteration. According to the method, the registration precision and the global optimality are ensured through SVD decomposition, the model volume shrinkage is effectively prevented by using Taubin bidirectional smoothing, the calculation efficiency is greatly improved through an adjacency list caching mechanism, and high-quality, high-efficiency and automatic three-dimensional model local feature transfer and fusion are realized.

This invention discloses a storage optimization method and system for crowdsourced querying of knowledge graphs in the field of Traditional Chinese Medicine (TCM). The method includes: storing TCM RDF triple data in the form of an adjacency list; constructing an uncertain edge index based on a hash table and a definite edge index based on a B+ tree for different patterns in crowdsourced queries, where the known predicate is not the subject / object or the known subject / predicate / object combination, with index entries pointing to an inverted linked list storing entity ID sequences; supporting dynamic maintenance of the adjacency list and indexes during incremental data updates; for unknown nodes in the query graph, retrieving the candidate entity ID set for each edge using a hybrid index, and calculating and returning the top K candidate nodes with the minimum overall cost based on a preset crowdsourcing cost using a Top-K matching algorithm based on a max-heap. This invention effectively solves the problems of low storage efficiency, index mismatch, and poor update scalability in TCM knowledge graph crowdsourced query scenarios, achieving efficient storage, fast querying, and low-cost crowdsourcing.

The invention discloses a data structure conversion method and device, electronic equipment and a storage medium, and relates to the technical field of databases. The method comprises the steps of obtaining a data table of a relation structure with an incidence relation; extracting at least one row data from the data table, and determining at least one graph node; obtaining foreign keys existing in primary keys in the data table, and determining graph nodes according to row data of the foreign keys and graph edges between the graph nodes generated by the fields with the incidence relation; aiming at each generated graph edge, generating a row positioning identifier according to the physical address of the row data of the graph edge index, and adding the row positioning identifier into the relation attribute of the graph edge; generating an adjacency list according to each graph node and each graph edge; and determining each graph node, each graph edge and the adjacency table as graph structure data of the data table. According to the embodiment of the invention, the graph structure data conversion accuracy and the depth query efficiency can be improved.

A system comprises one or more processors and non-transitory computer-readable media storing computing instructions that, when executed, perform operations comprising: generating an adjacency list for candidate items using a distance threshold with the maximum allowable neighbor distance equal to a Siamese model cut-off. The operations can also comprise loading data for the candidate items in the adjacency list and generating graphs of the candidate items in the adjacency list. The operations further can comprise determining, using breakdown logic, first graphs of the graphs that exceed a predetermined size, and building hierarchy dendrograms of nested subclusters of the first graphs. The operations additional can comprise determining cut-off values based on p-th percentiles of density for the first graphs, and identifying recommended variant groups of the candidate item in the nested subclusters of the hierarchy dendrograms below the cut-off values. Other embodiments are disclosed.

The invention relates to methods, storage media, and vehicles for navigating an optimal path. Among other things, techniques are described for generating a collision-free path by connecting C-patches via cell decomposition. The environment is sampled at discrete headings of the vehicle to generate a configuration space (C-space) with one or more C-patches. A first C-patch is decomposed into one or more cells representing free space. A C-patch adjacency list is generated for the first C-patch. A super-adjacency list is derived that connects vertices of interest across one or more C-patches to form a super-adjacency graph. In embodiments, Dubins paths are used to connect vertices of interest within and across C-patches to ensure kinematic feasibility of all searched paths. An optimal path is navigated, where the optimal path is the shortest path on the super-adjacency graph from a start pose to a goal pose.