Network Perspective of Leaders (A review)

What a leader should know about the network

Leadership has been taken into account in an individualistic perspective since many years. Qualities of leadership have been considered in a micro-oriented manner. Available underlying assumptions that put the leader in the spot light by leaving all the rest outside will not proceed further due to the interconnectedness nature of growing society. How an individual ( i.e. a leader) will be able to influence the mass or which individual will have a tendency to influence the mass are totally different perspectives in the identification of leaders.

In future a leader should be equipped with the information of “network perspective” of the organization, or society. The network perspective of an organization tells us who influence whom? Who supports whom? Who trust on whom? Friends in the set up? Who conflicts with whom? Performance and viability of individuals and groups, information flow pathways, bottlenecks of responsibilities and many more.

Identification and strengthening communication ties with influential individuals is important for a leader to develop two way ‘trafic’ (information). An individual could be important as acting like a bridge between communities and can be tagged as ‘key integrator’, and on the other hand an individual could be a crucial such that if we will pick out the group will be divided on multiple groups in terms of ‘trust’.

Consider the following cases: We have three different topologies of interactions within different groups, who is the most important individual in each group? Does it make any sense or not?

Comment on the above 3 Figs 1a, 1b, and 1c: which individual is most important in each group?

Is there any individual leader/influential person present in each group who really influence the others?

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Ins Network Advisor

Social Network Analysis: A tool for interaction and behavior modeling

We are living in a combination of closely knitted networks, just like a spider web.We exit a network only to enter into another one. Relationships, interactions, and likeness and dis likeness can be represented with the help of graphs or networks. At home, our interactions with wife, parents, children, neighbors and relatives can be represented as nodes with edges in a graph.

At business, like in a supply chain, a supplier provides supplies to other entity that could also be a supplier to another entity. These interactions can be represented through graphs as well. Moreover, our trust, discussion, friendship attitudes at our offices can also be represented through networks and graphs.

These graphs or networks are just like our friendship interactions at face book and are named social networks as they are based on our social relationships. Identification of influential individuals/groups, group of people having common interest, people acting as bridges between two communities all can be done through social network analysis (SNA). SNA tools enable us to identify structures within graphs, important nodes, paths (communication, responsibilities, information flow) and many more.

For e.g.

Consider a protein-protein interaction(PPI) network, in a bacteria a PPI consists on about 4000 protiens. Which proteins are the most important in all others? which set of proteins are most connected (max interaction with each other making clique)? which protein causing series of actions by acting as a bridge between two communities? and many other issues can be solved through SNA tools.

Figure. In the C. elegansinteractome, single proteins (circles) partner up (indicated by lines) to form an interconnected network that controls cell function. The inset details a small part of the network. (Image courtesy of Marc Vidal)[1]

We will discuss Complex Networks and SNA in next posts.

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Ins Network Advisor

[1]http://proteinfunction.net/index.php/About_PPI_Maps

Computational Social Science and Social Network Analysis Resource

In this blog I would like to share some important links that could be a great resource for a novice in Computational Social Science:

Prominent Groups and Research Institutes on Social Network Analysis and Computational Social Sciences:

George Mason University

Santa fe Institute

Institute for Quantitative Social Sciences, Harvard University

Human Complex Systems, UCLA

MIT Reality Mining

About Social Network Analysis

University of Essex

International Network on Social Network Analysis

University of Greenwich

Fas Research

ERCIM Graph and Network Analysis Cluster

IBM Watson Research Center

IRE Social Network Analysis

Try these links, valuable resource will be helpful to you as a novice.

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Ins Network Advisor

Computational Social Science (CSS) an Introduction

We are living in the age of technology and information, our each and every action is being recorded in the form of data and advancement in technology has allowed us to transmit that data very quickly on long distances. If we are driving our car and take exit on a city our car number get recorded in the visual camera and our exit information saved in our movement, after eating pizza or pratha on some restaurant in other city when we pay the bills through the

credit or debt card our transaction will be the signature of our place, time and activity, similarly if we are on airport and checking our black berry or email on net café then our activity is being recorded and can be followed. All of these forms of data could be helpful to connect people of common interests, common objectives or facing similar problems. Analysis patterns of group behaviors sharing common attributes has become a new challenge which is very requires different from currently existing mathematical, computational, sociological, and psychological tools.
Establishing relationships between people, through technology, on the basis of common interests, behaviors, movements, or profession has been termed as development of social networks. These social networks can be based on hobbies like stamp collectors, supporters of same football team like Barcelona, following advancements in science and technology like Bioinformatics, or discussing social issues like marriage and divorce growth rate. The study of such interactions is evolved to a new field of science i.e. Computational Social Sciences.
Computational social science helps us to study such massive amount of data from different view points. Such as computer scientists and mathematicians are always interested in finding new patterns, structures and information present in graphs, irrespective to their meaning. However, a consumer marketing analyst is more interested in finding buying pattern of people sharing common interest on internet etc.

Computational social science is an interdisciplinary filed of mathematics, computers, management, and psychology. Computational social science deals with the data related to human relationships like trust in an organization for team building or friendship in a society for community development. Connections or relationships between people in an organization or society in terms of formal or informal is represented with the help of graphs. In such graphs nodes represent people and edges represent their relationships.

Applications of Computational Social Sciences (CSS)

1. Behavioral patterns can be identified.
2. Supply chain networks can be studied by several ways i.e. who supplies whom? Who can continue supplies if one or two of his suppliers failed? Etc
3. Human resource management systems like who is the key integrator in different groups within an organization? or who is the most influential person in the organization (can be extracted from informal networks)
4. Trend analysis by relating crimes, behavior and locations.
Many other domains can be covered with CSS. In this blog I will try to discuss such problems, their solution methodologies, results, with theoretical and experimental foundations.

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Ins Network Advisor