whose streets were

whose streets were

ผลลัพธ์ (ไทย) 1:

ถนนที่มี

ผลลัพธ์ (ไทย) 2:

2.3 Role of Social Network Metrics
Social metrics measures are powerful measures with which we are in position to
discover hidden relations between nodes. Each network metrics plays specific role
in discovering relations, as well as for important nodes recognition. During social
network analysis stage, analysts are in position to combine different metrics with
intention to reveal hidden knowledge [1, 2, 4]. Usage of social metrics combination
is not always an easy task, especially in situation where we would like to create
filters for network structure based on social network metrics [14]. Interpretation of
such defined social network metrics combination could be hard as well as ensuring
periodical analysis on same filters.
In conditions where we have social network data; it is valuable source for cognitive
computing in light of fraud detection [1–4, 7, 10]. Concentrations of influences
within nodes with some detected anomalies can be on help for automated fraud investigation.
For example if we have some suspicious activities in multimodal network
on node level, where suspicious activities are recognised as frequent events associated
with Benford’s law or/and extremes and also high value of social network
metrics like eigenvector this nodes are for sure worth of further deeper investigation.
Social network analysis can be applied only in situation where we are dealing
with social network data, and it contributes to higher accuracy in fraud detection.
From that perspective, social network analysis contributes also in synergy of different
elementswhich provides solution for complex and challenging problems [11, 12,
14–17]. Usage of social network metrics in fraud is well known in practice and literature.
From perspective of proposed solution it is useful element which contribute
to cognitive fraud detection solution along with Benford’s law, statistical measures,
united within fuzzy expert system as holistic system for fraud detection. Generally
speaking each social network metrics like degree centrality, betweenes, eigenvector
and other measures has great potential as itself in fraud detection. Enumerated
metrics by itself can be good milestones for deeper investigation of fraud if we are
talking about fraud detection models. As it is case with other mentioned techniques,
grounding hypothesis on single, or limited number of techniques or methods are not
sufficient, especially if we are dealing with cognitive fraud detection systems.

ผลลัพธ์ (ไทย) 3:

他们的街道