Authors :
Amey Thorat; Amulya Kura; Ansika Jaiswal; Sapana Survase; Amit Aylani
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://rb.gy/sdzbh
DOI :
https://doi.org/10.5281/zenodo.7960559
Abstract :
Social media algorithms are used for finding
detailed information in large unstructured data by
relevant keywords used by users. There are different
algorithms used for social media from a searching point
of view. One of the algorithms is the "Probability of
Node's Degree" algorithm, which is based on the concept
of breadth-first search, random walk, and the highest
degree seeking algorithm. The algorithm involves
selecting a source node and a target node, and then
traversing the nodes in the network to find the target
node. The algorithm checks if the target node is a
neighbor of the current node and, if not, transmits a
query message to other nodes based on their probability
of being relevant to the search. Nodes with higher degrees
are more likely to be searched, making the algorithm
beneficial to nodes with higher degrees. In addition to
this, there are other algorithms such as FP-FOREST,
DSTree, UPTree algorithm, and KC-LA, which are used
for finding frequent patterns, maintaining and mining
frequent item sets, and finding K-Clique in complex
social networks. These algorithms are useful in datadriven decision-making and in gaining insights into social
media analytics.
Keywords :
Social Media Algorithm, Social Media Analytics, Complex Social Network, Social Media, K-Clique, Learning Automation, Betweenness Centrality, Random Walk.
Social media algorithms are used for finding
detailed information in large unstructured data by
relevant keywords used by users. There are different
algorithms used for social media from a searching point
of view. One of the algorithms is the "Probability of
Node's Degree" algorithm, which is based on the concept
of breadth-first search, random walk, and the highest
degree seeking algorithm. The algorithm involves
selecting a source node and a target node, and then
traversing the nodes in the network to find the target
node. The algorithm checks if the target node is a
neighbor of the current node and, if not, transmits a
query message to other nodes based on their probability
of being relevant to the search. Nodes with higher degrees
are more likely to be searched, making the algorithm
beneficial to nodes with higher degrees. In addition to
this, there are other algorithms such as FP-FOREST,
DSTree, UPTree algorithm, and KC-LA, which are used
for finding frequent patterns, maintaining and mining
frequent item sets, and finding K-Clique in complex
social networks. These algorithms are useful in datadriven decision-making and in gaining insights into social
media analytics.
Keywords :
Social Media Algorithm, Social Media Analytics, Complex Social Network, Social Media, K-Clique, Learning Automation, Betweenness Centrality, Random Walk.
