Sentence examples for influence degrees from inspiring English sources

After that, the choice of influence degrees and belief masses initialization are performed.

We first use the function giving the correspondences between the influence degrees, then we calculate the conjunctive combination.

The set of final influence degrees ({ {{mathrm{Inf}}}_u : u in U}) is denoted by ({{mathrm{Inf}}}).

The links are labeled with influence degrees (e.g., Weak, Average, and Strong) and belief masses (m_r) that depend on the type of the relation.

Thus, for users who have many combinations, they pass from the weaker influence degree (V.Weak) until they reach high influence degrees.

We use the following order of influence degrees ranking: (Omega< textsf {V.Weak}< textsf {Weak}< textsf {Average.E}< textsf {Average}< textsf {Strong.E}< textsf {Strong}< textsf {V.Strong} < textsf {E.Strong}).

After that, we rank users by their "maximal influence degree".

After that, we rank candidates by their "maximal influence degree".

We deduce all the candidates ranking by influence degree.

When two users have the same "maximal influence degree": (Inf({''U_1"}) = textsf {V.Strong}) and (Inf({''U_2"}) = textsf {V.Strong}) we compare belief masses of the next-greater influence degree and rank them according to the next-greater influence degree.

Each relation is associated with an influence degree (d_r) for (rin R), for example, the relation retweet is associated with the influence degree (d_{retweet} = V.Weak).