Rabu, 15 Februari 2012

Social Network Analysis Keilmuan Cabang Dari Sosiologi untuk Kegiatan Analisis Bussiness Intelegence

Semua orang di dunia ini yang sudah terbiasa dengan internet rata-rata pasti memiliki akun sosial media, baik itu facebook ataupun twitter. Ini juga menjadi sebuah keilmuan baru untuk para kalangan intelektual yang berkaitan dengan prospek bisnis pelanggan dan calon pelanggannya. Lahirlah Social Network Analysis yang merupakan cabang keilmuan sosiologi pada graph tak berararah yang sering dipakai untuk sosial media. Ambil contoh facebook. Facebook yang kita ketahui kita bisa membangun aplikasi diatasnya ataupun terpisah daripadanya. Dengan data yang bisa diambil dari facebook inilah yang akan kita bisa analisis lanjutannya mengunakan SNA ( Social Network Analysis). Tentunya untuk mengukur sesuatu harus ada alat ukurnya, adapun metric atau alat ukur SNA itu sendiri yang diambil dari wikipedia antara lain :
The extent to which a node lies between other nodes in the network. This measure takes into account the connectivity of the node's neighbors, giving a higher value for nodes which bridge clusters. The measure reflects the number of people who a person is connecting indirectly through their direct links.[20]
An edge is said to be a bridge if deleting it would cause its endpoints to lie in different components of a graph.
This measure gives a rough indication of the social power of a node based on how well they "connect" the network. "Betweenness," "Closeness," and "Degree" are all measures of centrality.
The difference between the number of links for each node divided by maximum possible sum of differences. A centralized network will have many of its links dispersed around one or a few nodes, while a decentralized network is one in which there is little variation between the number of links each node possesses.
The degree an individual is near all other individuals in a network (directly or indirectly). It reflects the ability to access information through the "grapevine" of network members. Thus, closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network. (See also: Proxemics) The shortest path may also be known as the "geodesic distance."
Clustering coefficient
A measure of the likelihood that two associates of a node are associates themselves. A higher clustering coefficient indicates a greater 'cliquishness.'
The degree to which actors are connected directly to each other by cohesive bonds. Groups are identified as ‘cliques’ if every individual is directly tied to every other individual, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted.[21]
The count of the number of ties to other actors in the network. See also degree (graph theory).
(Individual-level) Density
The degree a respondent's ties know one another/ proportion of ties among an individual's nominees. Network or global-level density is the proportion of ties in a network relative to the total number possible (sparse versus dense networks).
Efficient immunization strategy
The acquaintance immunization strategy, propose to immunize friends of randomly selected nodes. It is found to be very efficient compared to random immunization.[22]
Flow betweenness centrality
The degree that a node contributes to sum of maximum flow between all pairs of nodes (not that node).
Eigenvector centrality
A measure of the importance of a node in a network. It assigns relative scores to all nodes in the network based on the principle that connections to nodes having a high score contribute more to the score of the node in question.
Human interaction
Links in social networks are formed through human interactions. Scaling laws in human interaction activity were found by Rybski et al.[23]
Influential Spreaders
A method to identify influential spreaders is described by Kitsak et al.[24]
Local bridge
An edge is a local bridge if its endpoints share no common neighbors. Unlike a bridge, a local bridge is contained in a cycle.
Path length
The distances between pairs of nodes in the network. Average path-length is the average of these distances between all pairs of nodes.
In a directed graph prestige is the term used to describe a node's centrality. "Degree Prestige," "Proximity Prestige," and "Status Prestige" are measures of Prestige. See also degree (graph theory).
Degree an individual’s network reaches out into the network and provides novel information and influence.
The degree any member of a network can reach other members of the network.
Second order centrality
It assigns relative scores to all nodes in the network based on the observation that important nodes see a random walk (running on the network) "more regularly" than other nodes.[25]
Structural cohesion
The minimum number of members who, if removed from a group, would disconnect the group.[26] The relation between fragmentation (Structural cohesion) and percolation theory is discussed by Li et al.[27]
Structural equivalence
Refers to the extent to which nodes have a common set of linkages to other nodes in the system. The nodes don’t need to have any ties to each other to be structurally equivalent.
Structural hole
Static holes that can be strategically filled by connecting one or more links to link together other points. Linked to ideas of social capital: if you link to two people who are not linked you can control their communication.

Masing masing memiliki rumusan tersendiri, salah satu contoh user interface untuk analisis ini adalah :
Terlihat jelas bagaimana menggunakan SNA dari data yang diinputkan tersebut.

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