Search results
Results from the WOW.Com Content Network
A simple social network: the nodes represent people or actors and the edges between nodes represent some relationship between actors. Katz centrality computes the relative influence of a node within a network by measuring the number of the immediate neighbors (first degree nodes) and also all other nodes in the network that connect to the node under consideration through these immediate neighbors.
In this graph, n variable nodes in the top of the graph are connected to (n−k) constraint nodes in the bottom of the graph. This is a popular way of graphically representing an (n, k) LDPC code. The bits of a valid message, when placed on the T's at the top of the graph, satisfy the graphical constraints.
A node is a basic unit of a data structure, such as a linked list or tree data structure. Nodes contain data and also may link to other nodes. Links between nodes are often implemented by pointers. In graph theory, the image provides a simplified view of a network, where each of the numbers represents a different node.
One-shot timers will signal only once and then stop counting. Periodic timers signal every time they reach a specific value and then restart, thus producing a signal at periodic intervals. Periodic timers are typically used to invoke activities that must be performed at regular intervals.
If a node is a head of a matching edge, then this node is matched (green nodes in Fig.b). Otherwise, it is unmatched (white nodes in Fig.b). Those unmatched nodes are the nodes one needs to control, i.e. the driver nodes. By injecting signals to those driver nodes, one gets a set of directed path with starting points being the inputs (see Fig.c).
The time-to-digital converter measures the time between a start event and a stop event. There is also a digital-to-time converter or delay generator. The delay generator converts a number to a time delay. When the delay generator gets a start pulse at its input, then it outputs a stop pulse after the specified delay.
When working with graphs that are too large to store explicitly (or infinite), it is more practical to describe the complexity of breadth-first search in different terms: to find the nodes that are at distance d from the start node (measured in number of edge traversals), BFS takes O(b d + 1) time and memory, where b is the "branching factor ...
Because those nodes may also be less than half full, to re-establish the normal B-tree rules, combine such nodes with their (guaranteed full) left siblings and divide the keys to produce two nodes at least half full. The only node which lacks a full left sibling is the root, which is permitted to be less than half full.