Lack of appropriate infant and young child feeding (IYCF) practices is a significant contributor to child mortality rates worldwide. According to the World Health Organization (WHO), undernutrition is associated with 45% of child deaths [1]. These adverse effects of poor IYCF practices and undernutrition are worst for the poorest populations. In order to reduce the adverse effects of inappropriate IYCF and child undernutrition in this region, different non-government and academic organisations have been engaged in five South Asian countries (i.e. Sri Lanka, India, Nepal, Bangladesh and Pakistan) under the banner of the South Asian Infant Feeding Research Network (SAIFRN). As a part of their research mandate, SAIFRN used a social network research approach to understand the level of participation and engagement of different international and domestic stakeholders in relation to funding and technical support activities for infant and young child nutrition (IYCN) in these five South Asian countries.
A social network is a group of actors that are linked together by a set of social relations [2]. These relations describe the ties of a specific kind among the actors of the network. Actors of a social network can be individuals, organisations or companies. Regardless of what they are, they are always the smallest single unit inside a network. In a visual illustration of a social network, actors are presented by nodes and relations among actors are presented by links. For example, Fig. 1 shows a friendship network where actors are the four individuals who are represented by four nodes labelled with A, B, C and D. The links among them represent their friendship ties. Social networks can also be thought of as neighbourhoods since networks are comprised of the actors and the relationships between those actors. The formation of a social network is typically associated with the need for an actor to receive some sort of information or resource from others; thus creating an exchange whereby actors invest in relationships determined by their level of needs [3].
The key principles that make the social network as a distinct research perspective within the social and behavioural sciences are: (i) actors and their actions in a social network are viewed as interdependent rather than independent, autonomous units; and (ii) relational ties between actors are channels for transfer of resources (either material or non-material) [2]. In order to explore a health related social science research question (e.g. women’s health care seeking behaviour in pregnancy in a low socioeconomic urban community), a standard social science research approach usually defines a population of relevant units (e.g. pregnant women), takes a random sample of them, if the population is quite large, and then measures a variety of characteristics (e.g. education, household economic status, previous behaviours, and residence). The key assumption for such a social science approach is that the behaviour of a specific unit does not influence any other units; and thus, ignores the relational information among underlying units of the context [4]. Unlike social science approaches, a social network approach always gives importance to the relationships among units in a study. For this reason, a social network does not emphasise individual actors and their attributes, instead it focuses on the relations among actors. The principal task in a social network study is therefore to understand structural properties (e.g. which individual is highly connected within the network) of social units and how these structural properties influence observed characteristics (e.g. decision-making skill) and associations among characteristics.
The location of an actor inside a social network can be an indicator of the strength of ties associated with that actor. An individual near the centre of a friendship network often has more ties or links between herself and the other actors, as opposed to someone on the outer fringes of that friendship network. A person on the outer edge of the network could be connected to the network by only one link. A social network analysis is a commonly followed analytical process in the ‘Network Science’ literature, which is used to map the connections among actors and measure and visualise their relationships in a social network [5]. A social network analysis of a social network provides both a visual and a mathematical analysis of network relations among actors within that network. Because of its ability to assess connectivity patterns of networks and network behaviour of their member actors, the usefulness of the application of social network analysis has already been appreciated across many disciplines, including health analytics [6], disease prediction [7, 8], co-author network [9], organisational science [10, 11] and anthropology [3, 12].
In order to represent the description of networks compactly and systematically, the social network analysis approach follows both graphical and mathematical techniques. Graphical techniques are used to visualise a given social network in terms of nodes and their connections. On the other side, mathematical techniques are applied to explore the structural properties of social networks.
Using graphical techniques of social network analysis, many network and non-network characteristics of actors and edges can be visualised. Researchers mostly use label, size, shape and colour of actors and thickness of edges in order to visually represent various non-network and network characteristics of actors of a social network [2, 13]. Labels are usually used to indicate names of the underlying actors. The size of an actor in a social network can be used to represent its network characteristic (e.g. degree centrality). If the actor has a higher degree centrality then its size will be bigger and vice versa. The shape and colour of actors in a social network can be used to represent both non-network and network characteristics of actors. In order to visualise an inter-organisational network, for example, square and circle shapes may be used to represent all government and non-government organisations, respectively. For the same purpose, anyone can use two different colours instead of two different shapes. On the other side, any of these two visual features (i.e. shape and colour) can be used to represent groups of actors that share similar structural properties. A set of actors form a network community if they are densely connected among themselves and sparsely connected with other network actors. All member actors of a social network community can be presented, for example, by the same colour or shape. The thickness of an edge that connects two actors can be used, for example, to represent the strength of relations between those actors.
Based on diverse mathematical techniques, researchers proposed many social network analysis measures to quantify structural properties of the actors of a network and the network itself [2, 14]. These measures have successfully been applied to explore networks and their participants by evaluating locations of actors in networks (e.g. [13]). One of the basic measures of the social network analysis is the network centrality, which is a structural attribute of nodes in a network. This attribute determines the relative importance of an actor within a network (e.g. how important a person is as an advice provider within her friendship network or how well-used a road is within an urban network). The selection of social network measures to study a network mainly depends on the social network research question under consideration. There are three primary measures of the network centrality: (i) degree centrality (representing activity of actors and their popularity in a network); (ii) representing closeness centrality (reachability of actors from other actors in a network); and (iii) betweenness centrality (representing actors’ control over the information flow in a network). Each of these measures addresses different structural characteristics associated with actors to assess their level of centralisation within the network.
This article follows a social network analysis approach to explore funding and technical support networks of IYCN programmes in Sri Lanka, India, Nepal, Bangladesh and Pakistan. In particular, four measures of social network analysis (in-degree, out-degree, closeness and betweenness) have been used to analyse funding and technical support networks among different stakeholders in these five South Asian countries.
