### The If a substructure occurs frequently, it is

The main approaches which are
followed for mining of graph data are

• Mining frequent sub graphs

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• Classification

• Clustering

2.1.1
Mining
Frequent Sub Graphs:

Frequent sub graphs, are sub
graphs that occur frequently in data represented as graphs. Frequent Sub graph
Mining (FSM) is the graph mining essence. To extract all the frequent sub graphs,
in a given data set, whose occurrence counts are above a specified threshold is
the objective of FSM. Other than the research activity associated with FSM is
also reflected in its many areas of its application is the development of FSM.
They are useful for characterizing graph sets, discriminating different groups
of graphs, classifying and clustering graph sets, building graph indices and
facilitating similarity search in graph data bases2. A substructure may be different
structural forms such as trees, graphs or lattices, which may be combined with
item sets or subsequences. If a substructure occurs frequently, it is called a
(frequent) structured pattern. Although graph mining may include mining
frequent sub graph patterns, clustering, graph classification and other analysis

2.1.2
Classification:

Classification is a general process related
to categorization, the
process in which ideas and objects are differentiated, recognized and
understood. A classification
system is an approach to manage classification. Classification
is the method of discovering a representation that demonstrates and
distinguishes data classes or ideas, for the inspiration to use the model to
predict the class of objects whose class label is unknown. The model is derived
is based on the analysis of a set of training data. There are various other
methods for constructing classification models, such as Naive Bayesian
classification, k-nearest neighbor classification and support vector machines.

2.1.3
Clustering:

Clustering or Cluster analysis  is the task of grouping a set of
objects in such a way that objects in the same group are more similar (in some
sense or another) to each other than to those in other groups. Cluster is a group of objects
that belongs to the same class. In other words, similar objects are grouped in
one cluster and dissimilar
objects are grouped in another cluster.
This problem is challenging because of the need to match the
structures of the underlying graphs, and use these structures for clustering
purposes.

2.2       Tools:

There are several tools available for
graph mining. Some of them are given here.

2.2.1
Cytoscape:

Cytoscape is open source software
platform for visualizing molecular
interaction networks and integrating with gene expression profiles and other state data.
Additional features are available. Plug in are available for molecular
profiling analyses, new layouts, additional file format support , networks and
connection with databases and searching in large networks. Plug in may be
developed using the Cytoscape open Java software
architecture by anyone and plug in community development is encouraged. It is
used for Cytoscape is most
commonly used for biological applications, it is agnostic in terms of usage.
Cytoscape can be used to visualize and analyze network graphs of any kind
involving nodes and edges (e.g., social networks). A key aspect of the software
architecture of Cytoscape is the use of plug in for specialized features. Plug in
are developed by the greater user community and core developers.

2.2.2
Gephi:

Gephi is
an open-source software for network analysis and visualization. It helps data
analysts to intuitively reveal patterns and trends, highlight outliers and
tells stories with their data. It uses a 3D render engine to display large
graphs in real-time and to speed up the exploration. Classic
metrics of social network analysis, such as node degree or betweens centrality
measures, can be computed and used in the visualization as well. The network
can also be altered based on attributes.

2.2.3
Graph Insight:

Graph Insight is a visualization software that lets you explore graph
data through high quality interactive representations. knowledge extraction and
data exploration from graphs is of great interest nowadays. Knowledge is
platforms. Humans are extremely good in identifying outliers and
patterns. Graph Insight is useful for interacting visually with the data can
give us a better intuition and higher confidence on the field.

2.2.4
Network X:

Network X is a Python package for the creation, dynamics manipulation,
study of the structure, and functions of complex networks.
Flexibility ideal for representing networks found in many different fields.

2.2.5 Social Networks Visualizer:

Social Network Visualizer (SocNetV) is a cross-platform, user-friendly free software
application for social network analysis
and visualization. Edit actors and ties through point-and-click, analyze graph
and social network properties,
produce beautiful HTML reports and embed visualization layouts to the network.

2.2.6 Knime:

KNIME, the Konstanz Information Miner, is an open source
data analytics, reporting and integration platform. KNIME integrates various
components for machine learning and data mining through its modular data
pipelining concept.

3 . Issues :

• Scalability

• Data Ownership and Distribution

• Dimensionality

• Privacy Preservation

• Streaming Data

• Complex and Heterogeneous Data

4.  Conclusion:

In
this paper we briefly discuss about the graph mining techniques, tools and
issues from its initiation to the upcoming research. This paper provides a new
perspective of a researcher to overcome the challenges in methods, data and
other issues of graph mining in social network.

5.
References:

1.
Nettleton DF. Data mining of social networks represented as graphs. Elsevier.
2013; 7:1–34.

2. Du H.
Data Mining Techniques and Applications an Introduction, 1st Edition. Cengage
Learning       Edition; 2010.

3. Han J,
Kamber M. Data Mining: Concept and Techniques, 2nd Edition. Morgan Kauffmann;
2006.

4. Chen
MS, Han J, Yu PS. Data mining: an overview from database perspective. IEEE
Transactions on Knowledge and Data Engineering. 1999 Dec; 8(6):866–83.