1.
Ali Anani
SOCIAL NETWORK ANALYSIS OF
INTANGIBLES

2.
INTRODUCTION- VISUALIZING INTANGIBLES
 Social Network Analysis (SNA) and
Organizational Network Analysis (ONA) are
continuously gathering momentum in studying
relationships among agents or actors of these
networks
 I noticed the scarcity of applying SNA and ONA
in networks in which actors are not
humans, but intangible factors such as values
and emotional intelligence.

3.
TWO EXAMPLES OF POTENTIAL APPLICATIONS
 I wish to introduce two ideas for analysis by
using the tools employed in SNA
 One example is related to the study of
organizational performance
 A second example is related to the study of
human emotions and their interactions
 I employ a different technique than those I
used in three previous presentations(1, 2, 3)

4.
ANALYSIS OF ORGANIZATIONAL PERFORMANCE
 Donald Clark Donald published an impressive
chart showing the linkages of factors affecting
organizational performance.
 I reproduced the chart in a SNA format to allow
for the study of the interacting parameters
using NodeXL Excel Template developed by
Microsoft.

5.
THE RATIONALE
 Soft skills are receiving widening attention for
their role in enhancing organizational
performance. As we talk about centrality of
human agents; by the same token we may
discuss the motivators that cause these
actions.

6.
THE PERFORMANCE NETWORK
 In this presentation a directed relationship has
been assumed between all factors that interact
to yield the observed performance.
 EI stands for Emotional Intelligence
 The resulting network shows the type of
interactions and their consequences
 The size of vertices is proportional to the
Closeness Centrality

7.
THE PERFORMANCE NETWORK- 2

8.
THE PERFORMANCE NETWORK- 3
We have four
clusters with
components
of each
cluster
sharing the
same color in
this “Circle”
arrangement.

9.
THE PERFORMANCE NETWORK- 2
The overall metrics of the network are:
Maximum Vertices in a Connected Component 15
Maximum Edges in a Connected Component 19
Maximum Geodesic Distance (Diameter) 7
Average Geodesic Distance 3.09
Graph Density 0.09

10.
REMARKS
 The low graph density (0.09) suggests the
possibility of having more interactions among
performance agents
 There exists differences in other parameters
such as Betweenness Centrality, Closeness
Centrality, Ejgenvector Centrality and Clustering
Coefficient. See next slide. Refer in particular to
the differences in the Closeness Centrality

11.
REMARKS- 2 SHOWING IMPORTANCE OF THE
CLASSIFYING FACTOR CLOSENESS CENTRALITY
6.000
5.000
4.000
Betweenness Centrality
3.000 Closeness Centrality
Eigenvector Centrality
Clustering Coefficient
2.000
1.000
0.000
0 10 20 30

12.
THE HAREL-KOREN FAST MULTISCALE- ANOTHER
FACET OF THE NETWORK

13.
QUICK INSPECTION
 Inspection of the performance network factors
may reveal areas that deserve rethinking. For
example, that there exists no direct linkage
between engagement and values may deserve
a second thinking.

14.
EMOTIONS AND THEIR INTERACTIONS
 Disagreements on emotions are
common, including the agreement on basic
emotions whether there are six or eight of basic
emotions. The transformation, mixing and
overlapping of emotions are also topics open
for varying opinions. For excellent references
refer to Personality and susceptibility to
positive and negative emotional states and
Anger and Disgust: Discrete or Overlapping
Categories?

15.
SUMMARY OF FINDINGS
The first eight rows
comprise the eight basic
feelings with their edges
colored in orange in the
next slide
To verify the mixing of two
emotions such as Fear +
sadness to give surprise
a directional relationship
was drawn, and in this
case from fear to surprise
and sadness to surprise

16.
THE CIRCLE ARRANGEMENT OF FEELINGS WITH
VERTICES SIZED TO CLOSENESS CENTRALITY

17.
A SECOND VIEW: HAREL-KOREN FAST
MULTISCALE

18.
ADDING A DYNAMIC FILTER
By increasing
closeness
centrality from 1.7
to 2.15 these are
the remaining
feelings

19.
AN EXTRA GRAPH FFOR CLARITY OF THE
NETWORK
Emotional
clusters-
spheres with
same color
include the
components of
each cluster

20.
THE GREED – FEAR QUADRANT
 The next slides elaborate on the greed – fear
quadrant and the possibility of producing
different paths (bifurcation) depending on
which quadrant we are in. These slides show
the complexity of emotions as well.
 Such graphs with the aid of emotional network
analysis may help in uncovering the way
emotions interact.
 SNA are for intangible factors as well

21.
THE GREED – FEAR QUADRANT
High High fear, high
fear, low greed
greed
Fear
Fear-Greed
Quadrants
Low Low
fear, low fear, high
greed greed
Greed

22.
Surprise +
Fear yields
awe
High fear +
acceptance (loss of High fear, high greed
greed) yields to Fast bifurcation between self-control
contempt and lack of it. Rate of bifurcation may
Fear lead to complex behavior
Fast bifurcation between self-control and
lack of it. Rate of bifurcation may lead to
complex behavior
Low fear, high greed
Low gives
fear, low High anticipation Surprise mixed
greed with joy yielding with sadness to
optimism yield
disappointment
Greed