Social Network Mapping and analysis
Raffaele Giordano and Alessandro Pagano (CNR-IRSA)
There is growing awareness that fast response to emergencies requires effective coordination among several institutional and non-institutional actors. Several evidences demonstrated how the effective coordination in emergency management requires a deep understanding of the complex network of interactions taking place during the different phases of the emergency management. To this aim, a method based on social network mapping and analysis has been developed and tested in EDUCEN case studies.
This section aims at emphasizing the usability of the proposed tool and at describing the different phases for its implementation in other case studies.
The experiences carried out in EDUCEN case studies, and specifically L’Aquila and Lorca CS, demonstrate the usability of SNM for addressing three different issues related to the coordinated interactions in emergency management:
- Identify all the actors that need to be integrated in the emergency management procedures: the analysis of the map of interaction allows the proposed method to detect the actual role played by the different actors – both institutional and non-institutional – during the emergency management. In a network of interaction, crucial roles are played by the actors that could facilitate the interaction and/or enhance the effectiveness of information sharing processes.
- Assess the level of accessibility of crucial information in case of emergency: if crucial information is not accessible to central actors then cooperative tasks performance could be hampered. Therefore, it is of utmost importance, for an effective emergency management process, to enhance the comprehension of the actual use and accessibility of the different information.
- Evaluate the congruence between the information sharing process and the cooperative tasks performance: the described method allows to identify the tasks that are shared among a high number of actors, and the tasks that require access to a high number of information
The SNM method is structured in two main phases:
- Collection and structuring of the local and experts’ knowledge about the interactions – both formal and informal – taking place during the emergency management process: in this phase a sequential implementation of storytelling approach (SA) and problem structuring method, specifically Fuzzy Cognitive Mapping (FCM), was implemented. The FCM allowed to translate the collected narratives into useful inputs for the SNA.
- Mapping and analysing the complexity of the interactions: the SNA was used to better comprehend the actual role played by the different actors – both institutional and non-institutional – in case of emergency, the tasks performed and the information each actor brings into the network. The SNA allowed to identify the potential vulnerabilities in the emergency interaction network.
The guidelines for the implementation of the SNM method are described in the “read-more” section.
The SNM tool has been implemented in two EDUCEN case studies, in order to analyse the complexity of the interaction network occurred during emergency situations: the 2012 episode of flash flooding in Lorca (Spain) and the 2009 earthquake in L’Aquila (Italy). The main aim was to support the local decision-makers and emergency responders in defining potential improvements of the current emergency management protocols accounting for the key elements and the key vulnerabilities identified through the analysis of the network of interactions.
The SNM implementation in Lorca has been particularly successful. The results of the SNM implementation were actually used by the local decision makers to discuss and revise the current protocol of intervention in case of flash flood. The analysis carried out in this work increased the decision-makers’ awareness about the role played by the informal interactions taking place within the institutional system and between institutional actors and the members of the community. Using the results of the key vulnerabilities analysis, participants started discussing about suitable strategies to improve the flood emergency management plan, accounting for the complexity of interactions. Specifically, the discussion initially focused on the role of the media. Most of the institutional actors agreed that enabling a more effective bi-directional communication with the community members through the social media would be beneficial for sharing emergency information. The institutional actors were interested in enhancing the capability of the current media channels to collect, store and analyse the feedbacks from the community. The capability of local communities to contribute to the monitoring of the emergency evolution was deemed important by the participants. Some initiative to enhance the community leaders involvement in the emergency management were initiated during EDUCEN implementation.
The success of the Lorca implementation enabled the replication of the SNM implementation to the flood emergency management in Valladolid (Spain).
Knowledge elicitation and structuring phase: Different steps can be identified:
- Identification of the actors to be involved: In order to minimise the selection bias of stakeholders a top-down stakeholder identification practice, which is referred as “snowballing”, is implemented. The selection process starts with the actors mentioned in the official protocols of intervention. The preliminary interviews carried out with these agents allow us to widening the set of stakeholders to be involved. The interviews should allow to collect individual experiences about i) the i) the emergency management, ii) the role of information exchange, and iii) the interactions taking place during a crisis. A storyline approach can be adopted. Referring to a specific episode, participants are required to describe the sequence of actions implemented in order to achieve their goals in the emergency management, the information used and the other agents with whom they interacted.
- Structuring of the interview in Fuzzy Cognitive Map (FCM):
Figure fuzzy cognitive map about here
Fig. 1: Fuzzy Cognitive Map describing the individual’s understanding of the connections between goal-task-information-agents
The weight of the link agent-information describes the interviewee’s perception about how crucial is the agent to obtain the needed information. Similarly, the weight of the link information-task represents the role played by the information in facilitating the implementation of that specific task. In order to facilitate the elicitation of the experts’ knowledge, fuzzy linguistic assessment – e.g. strong, medium, and weak – can be used to describe the strength of the connections.
Figure example of FCM about here
Fig. 2: Example of FCM
Mapping the network of interactions: The SNA phase focused on structural patterns between actors involved in emergency management, allowing the understanding of roles, interdependencies, tasks, and information flows, through specific measures. The Organizational Risk Analysis (ORA) approach (Carley, 20021) has been implemented, allowing the analysis of the emergency interaction network accounting for the role of knowledge and tasks, and of the interconnections among the key elements – i.e. agent, knowledge and tasks.
The interlocked networks can be represented using the meta-matrix conceptual framework, as shown in the following table 1.
|Agent||Social network: map of the interactions among the different institutional actors in the different DRR phase||Knowledge network: identifies the relationships among actors and information (Who does manage which information? Who does own which expertise?)||Assignment network: defines the role played by each actor in the DRR phases|
|Knowledge||Information network: map the connections among different pieces of knowledge||Knowledge requirements network: identifies the information used, or needed, to perform a certain task in the DRR|
|Tasks||Dependencies network: identifies the work flow. (Which tasks are related to which)|
Table 1: Meta-matrix framework showing the connections among the key entities of social network (adapted from Carley, 2005)
ORA method theorizes that the effectiveness of a social network is not limited to the way the different actors interact with the others. The meta-matrix framework allows to analyse the complexity of the emergency interaction network accounting for the role of knowledge and tasks, and of the interconnections among the key elements – i.e. agent, knowledge and tasks.
The Agent x Agent matrix is shown in the table 2.
Table 2: Agent x Agent matrix
In the previous matrix, Wij represents the importance of the interaction between the agent Ai and the agent Aj as perceived by the agent Aj. Similarly, the value of Wji refers to the strength of the interaction between the agent Ai and the agent Aj as perceived by the agent Aj. The weights can be assessed accounting for the experts opinion. In this work, we use the term “experts” to indicate policy-makers and official responders involved in the emergency management. The experts’ knowledge was elicited through a series of individual semi-structured interviews. A storyline approach (SA) was implemented. Referring to a specific episode of emergency management, participants were required to describe the sequence of actions implemented in order to achieve their goals in the emergency management, the information used and the other agents with whom they interacted.
The individual FCMs were also used to define the other matrices. For instance, the individual i-th Agent x Knowledge matrix was obtained considering the weights assigned by the i-th actor to the different agent-information connections. The Agent x Knowledge matrix for the i-th agent is represented in Table 3.
Table 3: Knowledge network matrix for the i-th agent.
The overall Agent x Knowledge matrix was obtained as the sum of the individual matrices. Similar processes were implemented to develop the Agent x Tasks, Knowledge x Knowledge, Knowledge x Tasks and Tasks x Task matrices. In order to facilitate the elicitation of the participants’ opinions about the importance degree, fuzzy linguistic variables can be defined. This method requires the identification of the linguistic labels used by the interviewees to describe the importance of the connections. The weights in the matrixes are used to develop the network. They represents the strength of the ties between two entities. Figure 2 and 3 shows, respectively, the social network and the knowledge network developed for the Lorca CS. network.
Analysing the network of interactions
The aggregation of the different matrices allowed us to obtain the map of the interactions taking place during an emergency and connecting agents, knowledge and tasks. The map was developed using the ORA© software (Carley, 20051), developed by the Centre for Computational Analysis of Social and Organizational Systems of the Carnegie Mellon University. Following the graph theory, the weights in the matrixes were used to represents the strength of graph edges, while rows and columns were labelled by graph vertices. Consequentially, the vulnerability elements were identified though the combination of different graph measures, and provides a mathematical approach for measuring the strength of ties.
|Network||Network measure||Assessment||Meaning in DRR|
|Agent x Agent||Total degree Centrality||Those who are ranked high on this metrics have more connections to others in the same network.||Individuals or organizations who are ‘in the know’ are those who are linked to many others and so, by virtue of their position have access to the ideas, thoughts, beliefs of many others.|
|Betweenness centrality||The betweenness centrality of node v in a network is defined as: across all node pairs that have a shortest path containing v, the percentage that pass through v.||Individuals or organizations that are potentially influential are positioned to broker connections between groups and to bring to bear the influence of one group on another or serve as a gatekeeper between groups.|
|Agent x Knowledge||Most knowledge||Assess the number of links between a certain agent and the different pieces of knowledge in the network.||An agent with a high value of most knowledge has access to a great variety of knowledge to be used in case of disaster.|
|Agent x Task||Most task||Assess the number of links between a certain agent and the different task that need to be carried out in case of emergency.||An agent with a high degree of most task plays a crucial role in the network due to her/his capability in performing different tasks.|
|Knowledge x Knowledge||Total degree of centrality||It calculates the importance of a certain piece of information according to the number of connected links.||The most central pieces of knowledge are those whose availability is crucial to make the other pieces of knowledge accessible.|
|Closeness centrality||Closeness is the inverse of the sum of distances in the network from a node to all other nodes.||The closeness centrality measure allows us to identify the information that could facilitate the process of information sharing.|
|Knowledge x Task||Most task||Assess the number of links between a certain piece of knowledge and the different task that need to be carried out in case of emergency.||The pieces of knowledge with a high value for this measure are fundamental for the effectiveness of the network, since without them a high number of tasks will be not carried out.|
|Task x Task||Total degree of centrality||It analyses the complexity of the connections within the task X task network.||Tasks with high degree of centrality are those that have to be carried out in order to allow the executions of the other tasks.|
Table 4: Graph Theory measures for key element detection
Different measures are mentioned in the scientific literature for the assessment of the network vulnerability, that is, those elements that could lead to failures of the network, lower performance, reduced adaptability, reduced information gathering, etc. (e.g. Carley, 2005). Considering the complexity of the emergency network, in this work the vulnerability elements were identified though the combination of different measures, as described in the table 5.
|Network||Network measures||Meaning in emergency management|
|Agent x AgentAgent x Knowledge||Total centrality degreeMost knowledge||An actor with a high degree of centrality and a low most knowledge degree represents a vulnerability because, although she/he a central position in the network, she/he has a limited capability to enable information sharing.|
|Agent x AgentAgent x Knowledge||Betweennes centralityMost knowledge||An actor with a high degree of most knowledge and a low betweennes degree represents a vulnerability because she/he is not capable to share with the others the pieces of knowledge she/he has access to.|
|Agent x AgentAgent x Task||Total centrality degreeMost task||An actor with a high degree of most task and a low centrality degree represents a vulnerability because, although she/he is required to carry out important tasks, she/he is quite isolated and cannot be supported by the others during an emergency.|
|Agent x KnowledgeKnowledge x Task||Most knowledgeMost task||A piece of knowledge poorly shared within the network (low most knowledge) represents a vulnerability if its access is crucial to carry out important task (high most task).|
|Agent x KnowledgeKnowledge x Knowledge||Most knowledgeCloseness centrality||A piece of knowledge with a high degree of closeness but poorly shared (low degree of most knowledge) represents a vulnerability since it could hamper the process of information sharing.|
|Agent x TaskTask x Task||Most taskCentrality degree||A task with a high centrality degree and with low most task degree represents a vulnerability because, although its importance, there is no, or very limited cooperation to guarantee its effectiveness.|
|Agent x AgentAgent x KnowledgeAgent x Task||Cognitive load||This measure takes into account the number of other agents, knowledge and tasks an agent needs in order to perform its own task. High cognitive load represents a vulnerability.|
Table 5: Measures for the detection and analysis of key vulnerability in the emergency management network