parallelEdge No Object How to process repetitive edges. action No String Processing method. The value isoverride, which indicates that the previous repetitive edges are overwritten. ignoreLabel No Boolean Whether to ignore labels on repetitive edges. The value isfalse. ...
In GraphLab, data is associated with both vertices and edges.Computation is then represented as a stateless program that is executed on each vertex of the graph in parallel. This program consists of three distinct phases: Gather, Apply, and Scatter (GAS)....
Graph states are a broad family of entangled quantum states, each defined by a graph composed of edges representing the correlations between subsystems. Such states constitute versatile resources for quantum computation and quantum-enhanced measurement. Their generation and engineering require a high leve...
2019), our methodology randomly partitions the hidden feature space in each layer, decomposing the global GCN model into multiple, narrow sub-GCNs of equal depth. Sub-GCNs are trained independently for several iterations in parallel prior to having their updates synchronized; see Fig. 1. This ...
If the independent variable is not expressly temporal, abar graphmay be used to show discrete numerical quantities in relation to each other. To illustrate the relative populations of various nations, for example, a series of parallel columns, or bars, may be used. The length of each bar wou...
,k}. Multiple edges are drawn in boldface and their multiplicities are indicated. On the left, the adjacency matrix of the nontrivial strong component is given. If β≥2, then the similarity between factor-graphs is even closer, allowing one to produce asymptotic formulas for the function α...
For example, the probability of an edge having type r ∈ Ce is equal to the fraction of edges in the training set of type r. We then use an approach motivated by Gibbs sampling to update graph components iteratively from the learned conditional distributions. At each generation step, ...
The alignment graph then has edges connecting the end of a node to the chopped boundary of the neighbor. This allows a path that ends at one node to enter the neighboring node without traversing the overlap twice. Figure4shows an example of the edge chopping for edges with variable overlaps...
The graph has Facebook- like size and distribution (800 millions nodes, 100 billion edges, with each node having on average 130 edges). We found that ex- ploring the entire 3-hop neighborhood of any node in the graph takes less than 100 milliseconds on average. In other words, Trin- ...
The graph is G = (V, E) where V is the vertices, and E the edges. The graph may be either directed or undi- rected. The vertices arrive in a stream with the set of edges where it is a member so for undirected graphs, each edge appears twice in the stream. We consider three ...