The Usage of Online Social Network (OSN) is increasing day by day where a large number of users publicly access their friend’s link and post any message on the wall. OSN enables different user’s to use the services with a security of using these messages on the user’s wall.
By Rahul Nema and Anjana Pandey
Department of Information Technology, SOIT, RGPV, Bhopal, India
Image Attribute: Social Network Links / Source: The NextWeb
Abstract:
The Usage of Online Social Network (OSN) is increasing day
by day where a large number of users publicly access their friend’s link and
post any message on the wall. OSN enables different user’s to use the services
with a security of using these messages on the user’s wall. Hence various
techniques are implemented for the filtering of these wall messages, but there
are still various issues and challenges found during the usage of these
techniques used in OSN. Here in this paper various issues and challenges from various
techniques are discussed and hence on the basis of various issues a new and
efficient technique is implemented in future.
A social network
can be explained as a social structure consisting of individuals or
organizations. These are related each other by some means. The social network
perspective provides a set of methods for analyzing the structure of social
entities and the theories thereby explaining the patterns in the structures.
These structures use social network analysis and identify local and global
patterns, locate influential entities and examine network dynamics. The
approaches used in social networks enable to know social interaction being
visualized first and investigated with the help of properties of relations
between the units and not the unit’s properties itself. The network
configurations and network analytics are formed by singular or combination
relations in social networks. Social networking service basically provides
platform for edifice of social networks or social relations between users
sharing interests, activities, backgrounds etc. Social network service provider
enables the user to maintain profile containing his or her social links and
other add ons.
Therefore with the help
of social networks, users are able to build a public profile and maintain list
of users for sharing connections and can cross and view the connections in the
system. Social network services are generally web based, facilitating the users
or the clients to interact over the Internet that can be in the form of e-mail,
instant message etc. Social network provides and allows using multiple
information and communication tools in the form of mobile connectivity, photo,
video, sharing etc.
Community services
provided by social networks are group centered and sometimes even social
networks are termed as individual centered service. Social networks are
generally divided into communities and groups which consist of users having
same likes, features, dislikes, interests etc. The main types of social
networking services are those which are categorized or divided in groups or
communities of users like schoolmates, politicians, celebrities etc. and a
giving a recommendation system linked with the trust of users. The widely used
Social networks are Facebook, Google+, YouTube, LinkedIn, Instagram, Pinterest,
Tumblr and Twitter.
Each user maintains its
own social network that may be online or offline and contains friends, families
and people whom they are acquainted with. The fundamental aim lying behind
online social networking services is to make users’ social networks visible to
others who are not connected to his/her immediate network. Social network held
people together by friendship, classmates, colleagues, business partners, etc.
having pre-estab- lished interpersonal relationships. The connections between
the users are generally formed one at a time. The primary reason that lies
behind the people is to join a social networking site for maintaining old
relationships with others and making new ones for expanding their network.
Social networks act as
unique service in which the users can collectively recognize others if they are
fake. The users also generally do not compartmentalize their life i.e. they
don’t have only one social network.
Communities in the
social network are held together by common interest among the users. The users
may possess common hobby for which the other community members may be
passionate, may have a common goal, similar lifestyle, geographical location
etc. Thereby in social networks users exhibit different influence and different
behavior [1] .
Internet contains
information of various types and kinds. For gaining knowledge from these data,
various users exist on internet and continue to use it. The users generally
share, disseminate and communicate multiple type of information between them.
The information is in the form of texts, audio, video, images etc. [2]
. These users may belong to multiple communities consisting of similar type of
users in behavior influencing each other for sharing various types of data.
Social Networks is
visualized in the form of internet service helping the user to build a networks
over the internet which is social in nature and builds relations with other
users for sharing of interests and participate in multiple activities with the
users who are characterized by communities.
Communities over the
social networks discover groups of interacting objects in the form of nodes
formalizing the relations between them. For example a community in a social
network can be defined as groups of friends attending the same school or people
of the same home town etc. [3]
. The communities correspond to users having similar scientific disciplines,
family, friends, similar interests etc.
Network community’s
helps in study of functionally related objects for analysis and for studying
interactions between the modules, predict unobserved connections etc. A
community structure property is contained by social networks which is explained
in the form of groups of vertices having denser connections inside each group
are divided and the fewer connections which crosses groups division where
vertices and connections represent network users and social interactions
respectively.
Members of communities
of a social network i.e. nodes share things between them in common like
interests in the field of photography, movies, music or political topics
thereby interacting more frequently with each other as compared with the
members outside of their community structure. Community detection therefore in
a network is explained on the basis of gathering of network vertices into
groups in which nodes in each group connect densely on the inner side and
sparsely on outer side [4]
[5]
.
Community kernel
detection problem has practical applications like representative user finding,
friend recommendation, network visualization, marketing etc. The problem being
considered as non-trivial in nature has a set of challenges in which true
influential user’s identification is hard. The number of followers of the
individual is used as indicator but its count contains no information about who
follows them in return. Influential users interacting with each other is
slightly non clear process and its process of taking place can be explained
with the example like there is half probability of an actress or any other type
of user to follow another actress or a sports person or politician etc.
Basically in real world
social networks with thousands of millions of nodes are increasing with rapid
growth. That’s why we need an algorithm with high scalability to resolve the
dilemma of community. For this, method kernel detection is required with
subtasks so that it can identify influential (kernel) members and detecting the
formation of community kernels.
The nodes (or vertices)
in social Networks are the typical systems which represent entities that have
some relationships. Web graphs, telecommunication networks, genetic networks,
trade networks etc. are examples of such systems other than social networks.
Generally the recognition of cluster of vertices within which the connections
(or edges) are numerous and between which they are rare are symbolized by
community detection or clustering.
The methods like
Spectral clustering are used for clustering which are basically based on the
eigen decomposition of a Laplacian matrix that is derived from the data. This
gave us an idea of being expansion of the clustering model to out of sample
nodes. Thus on a small subset of whole graph the clustering model can be
trained and visualized and so while dealing with huge and complex networks the
method could be applied to the rest of the network in a learning agenda
platform. The problem of online clustering of huge and raising networks can be
easily resolved with out-of-sample extension in the community detection field
algorithm. Thus when every new vertex is arriving in a data stream, it doesn’t
have to run on a new graph on the application of the above process [6].
Social influence affects
the social networks governing the dynamics of social networks. Users gets
influenced by other users when sharing information, exchanging data, messaging
etc. and forms communities in which users are influenced by similarities.
Social network analysis
focuses upon macro level models in the form of degree distributions, clustering
coefficient, communities, small world effect etc. Social influences usually
defines that a user may have higher influence over field or a topic than other
user and the other may higher influence with completely different field, topic
or interest. This arises a fact that users are needed to be analyzed based on influences
for forming community. Social influence does not explains the global measure of
importance of nodes or users and it only defines the measure on the links in
between nodes.
Conclusion:
Since online Social Network enables
various users to interact easily and quickly. Although there are various
techniques implemented for the community detection and message filtering but
the techniques implemented contain various issues and problems due to that it’s
efficient and accuracy decreases. Here in this paper a complete survey of all
such techniques is discussed and analyzed, hence on the basis of various issue
a new and efficient technique is implemented.
Cite this Paper:
Rahul Nema, Anjana Pandey (2016) A Review
on Online Social Network: Issues & Challenges. Social Networking,05,57-61. doi: 10.4236/sn.2016.52006
Copyright
© 2016 by authors and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons
Attribution International License (CC BY).
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