node i's connection weight vs node i's weight

Both phrases are correct, but they have slightly different meanings. 'Node i's connection weight' specifically refers to the weight of the connection associated with node i, while 'node i's weight' could refer to the weight of the node itself or the weight of a specific attribute related to node i.

Last updated: March 17, 2024 • 520 views

node i's connection weight

This phrase is correct and commonly used in the context of network analysis or graph theory to refer to the weight of the connection associated with a specific node.

This phrase is used to specifically refer to the weight of the connection associated with a particular node in a network or graph.

Examples:

Calculate the average of all node i's connection weights.
The algorithm assigns random values to the node i's connection weights.
The sum of node i's connection weights should not exceed a certain threshold.
The node i's connection weight is crucial for determining the shortest path.
The network optimization process involves adjusting the node i's connection weights.

Alternatives:

weight of the connection associated with node i
connection weight of node i
weight of the link connected to node i
edge weight of node i
link weight of node i

node i's weight

This phrase is correct and can refer to the weight of the node itself or the weight of a specific attribute related to node i, depending on the context.

This phrase can be used to refer to the weight of the node itself or the weight of a specific attribute associated with node i.

Examples:

Determine the node i's weight in the neural network.
The node i's weight is a crucial factor in the decision-making process.
The algorithm adjusts the node i's weight based on the training data.
The node i's weight influences the overall performance of the system.
The node i's weight should be normalized before further analysis.

Show examples from the web [+]

We also define node i's weight to be wi = ∑j∈NB(i) wi,j. For a player i ∈ A who does not have any neighbors in B we simply define wi = 0. We use G to denote ...
j,k candidate stop, all network nodes are candidates ri node i's weight measured with node random-walk centrality gi node i's weight measured with trip demand.
Node i's weight vector at time t w. (t+ 1. 2 ) i. Node i's update at time t. ˆw. (t+ 1. 2 ) i. Node i's approximate network average update. ¯wi. Sum of first t local weight ...
node i's weight increases beyond the threshold amount and thus triggers the DQEB algorithm. Thus node i tries to detach all its children and switch to being a ...

Alternatives:

weight of node i
node i's mass
node i's value
node i's importance
node i's significance

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node i's connection weight vs node i's weight