Dyad and Triad Census Analysis of Crisis Communication Network

doi:10.4236/sn.2013.21004

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Social Networking, 2013, 2, 32-41

http://dx.doi.org/10.4236/sn.2013.21004 Published Online January 2013 (http://www.scirp.org/journal/sn)

Dyad and Triad Census Analysis of Crisis

Communication Network

Shahadat Uddin*, Liaquat Hossain

The University of Sydney, Sydney, Australia

Email: *[email protected]

Received November 11, 2012; revised December 12, 2012; accepted January 11, 2012

ABSTRACT

Dyad and triad census summarize much of the network-level structural information of a given directed network. They

have been found very useful in analyzing structural properties of social networks. This study aims to explore crisis

communication network by following dyad and triad census analysis approach to investigate the association of mi-

cro-level communication patterns with organizational crisis. This study further tests hypothesis related to the process of

data generation and tendency of the structural pattern of transitivity using dyad and triad census output. The changing

communication network at Enron Corporation during the period of its crisis is analyzed in this study. Significant dif-

ferences in the presence of different isomorphism classes or micro-level patterns of both dyad and triad census are no-

ticed in crisis and non-crisis period network of Enron email corpus. It is also noticed that crisis communication network

shows more transitivity compared to the non-crisis communication network.

Keywords: Dyad Census; Triad Census; Crisis Communication Network; Organizational Crisis

1. Introduction

Communication networks are represented as patterns of

contacts which are created due to the flow of messages

among participating actors or communicators through

time and space. The word “message” encompasses eve-

rything (e.g. data, information, knowledge, image and

symbol) that can be co-created by network members and

that can flow from one point of contact to another within

and between networks. In contemporary organizations, a

communication network could take various forms such as

personal contact network, work related contact network,

strategic alliances among various firms, global network

of organizations, and flow of information within and

between groups [1].

By applying dyad and triad census analysis approach

this study explores the changing micro-level structure in

order to investigate patterns associated with the final

stage of a crisis communication network. The dyad cen-

sus seeks the tendency of reciprocity of relations within a

communication network. Frequencies of different micro-

level patterns are examined in triad census analysis. Both

dyad and triad census analysis have gained noticeable

popularity in recent social network research [2,3].

Crisis communication network of an organization is

the context of this study. A crisis communication net-

work can be defined as the network that has been evol-

ved among actors (e.g. staff) during an organizational

crisis period. Organizational crisis has been defined in

many ways by many different researchers such as organi-

zational mortality, organizational death, organizational

exit, bankruptcy, decline, retrenchment and failure to

characterize various forms of organizational needs [4].

Although there is limited consensus among researchers

on the precise definition of organizational crisis, there is

evidence of shared meaning. Hermann [5] defined crisis

as a situation that threatens goals of an organization, sur-

prises the decision makers by its occurrences, put them

under time pressure for appropriate responses and con-

sequently engender high level of stress. Milburn et al. [6]

identified several important elements of an organiza-

tional crisis such as: organizational crisis produces indi-

vidual crisis; crisis can be associated with positive or

negative condition; crises can be situations having been

precipitated quickly or suddenly or situations that have

developed over time; and crises are predictable. Weitzel

and Johnson [7] defined organizational crisis as a state in

which firms fail to anticipate, recognize, avoid, neutral-

ize, or adapt to external or internal pressures that threaten

the organization’s long term survival. Sheppard [8] de-

fined crisis as “a critical and irreversible loss by the sys-

tem” and posited that an organization dies when it stops

performing its expected functions. A drastic form of criti-

cal loss occurs when firms moves into bankruptcy as in

the case of Enron Corporation in the final quarter of 2001.

This study starts with the premise that email networks

*Corresponding author.

S. UDDIN, L. HOSSAIN 33

constitute a useful proxy for the underlying communica-

tion networks within organizations. A study by Smith et

al. [9] investigated how different age groups managed

their personal networks and what types of technology-

mediated communication tools they used. They found

that people around their 30s (25 - 35 years) used email

with the most of their social network contacts (81%).

60% of older age groups (50 - 60 years) also tended to

keep in touch with their personal contacts primarily by

using emails. Wellman [10] argued that computer sup-

ported social networks (CSSNs) sustain strong, interme-

diate and weak ties that provide information and social

support in both specialized and broadly-based relation-

ships. CSSNs support and foster both formal and infor-

mal workplace communities. Guimera et al. [11] argued

that the email network provides an inexpensive but pow-

erful alternative to traditional approach of survey which

is expensive and time consuming. Indeed, they found that

the exchange of email between individuals in organiza-

tions reveals how people interact and facilitates mapping

informal networks in a non-intrusive, objective and

quantitative way. Tyler et al. [12] described email com-

munication network as a tantalizing medium for research

which offers a promising resource for tapping into dy-

namics of information within organizations and for ex-

tracting hidden patterns of collaboration and leadership

that are at the heart of informal communities of practice.

As a modern and technologically advanced organization,

it is well known that employees of Enron (i.e. the re-

search subject of this study) used email as a significant

medium of communication.

This paper is organized as follows: in the next two sec-

tions dyad and triad census are discussed as a way to

analyze communication networks. An overview of Enron

email corpus that is analyzed in this study is described in

the subsequent section. This is followed by a discussion

of results and their implications. Finally there is a con-

clusion of this research.

2. Dyad and Dyad Census

A dyad consists of an unordered pair of actors and links

that exist between two actors of the pair. Dyads are

2-subgraphs where a subgraph is a subset of actors taken

from the complete set of network actors and all links

between them [13]. The dyad consisting of actors i and j

is denoted by Dij= (Xij, Xji), for i



j and where X repre-

sents the sociomatrix of the complete directed network

under consideration. For a network of size g, there are

exactly



ggg







 2 dyads.

The dyadic relation between any two actors in a given

network must have one of the three possible states or

isomorphism classes as illustrated in the Figure 1. By

= (0, 0) Null Dyad

= (0, 1) Asymmetric Dyad

= (1, 0) Asymmetric Dyad

= (1, 1) Mutual Dyad

Figure 1. Three dyadic isomorphism classes or states.

definition, two subgraphs are isomorphic if they are

identical. That means there exists a one-to-one mapping

among actors for these two subgraphs, except the possi-

bility of different labeling of those actors [14]. As pre-

sented in the Figure 1, a mutual relationship, denoted by

i  j, between actor i and actor j exists when i  j and j

 i in the dyad. In the sociomatrix X, the two symmetri-

cal cells (i, j) and (j, i) are unity (i.e. Xij = 1 and Xji = 1)

for this type of dyadic relation. That is why, the mutual

dyadic relation between actor i and actor j is represented

by Dij = (1, 1). The second state is the asymmetric dyad

which can occur in two ways: either i  j or j  i, but

not in both ways. The asymmetric dyadic relation be-

tween actor i and actor j is represented by Dij = (1, 0) or

(0, 1). In the sociomatrix X, one of the two symmetrical

cells (i.e. Xij and Xji) contains a 1 for this kind of dyadic

relation. Since the labeling in the sociomatrix is arbitrary,

it is not possible to distinguish two different forms (i.e. i

 j and j  i) of asymmetric dyadic relations. The third

state is the null dyad, in which neither actor has a tie to

the other. By default, a dyad that is not asymmetric or

mutual must be null. For null dyad, two symmetrical

cells (i.e. Xij and Xji) in the sociomatrix X contain a 0.

That means, for null dyad Xij = Xji = 0, and Dij = (0, 0).

If M, A and N are numbers of mutual, asymmetric and

null dyads in a collection of





2 dyads then these three

counts sum to





2 because they provide a complete

partition of the collection of dyads for any given directed

network of size g. The triple <M, A, N> is called the dyad

census. The frequencies of M, A and N can be calculated

directly from the element of the sociomatrix X for any

given directed network under study by the following

equations:

ij ji







(1)





(2)









 (3)

where, X++ = L, number of links in the network.

S. UDDIN, L. HOSSAIN

2.1. Katz and Powell Index for Mutuality

In order to measure the tendency for actors in a group to

reciprocate choices more frequently than it would occur

simply by chance, Katz and Powell [15] proposed an

index which was named according to their names—Katz

and Powell Index for Mutuality (kp). Like other statisti-

cal indices, this index is dimensionless and has the range

of − < kp  1; where 0 indicates no tendency for re-

ciprocation, 1 represents maximal tendency for recipro-

cation, negative values indicate tendencies toward asym-

metric and null dyads.

Based on the assumption that choices are made by ac-

tors in some random manner, Kat and Powell [15] nor-

malize this index for two particular network data collec-

tion designs: fixed choice and free choice. While no re-

striction is placed on the number of actors each actor can

relate to in a free choice design, the investigator gathers

data or instructs each respondent to name a fixed number

of others that the actor relates to on the relation under

study in a fixed choice design. For a fixed choice design,

if d is the fixed number of choices made by each of the g

actors then kp can be estimated by the following equa-

tion [15]:



ˆ1

Mgd

gd gd





 (4)

In free choice designs, choices made by different ac-

tors are not necessarily equal. Katz and Powell [15] de-

rived the following equation to estimate kp in a free

choice design:



ML L

LgL L







(5)

where, i is the total number of choices, and

is the sum of squares of the choices. And,

xi+ represents the number of choices made by the ith ac-

tor.







L

3. Triad and Triad Census

A triad or 3-subgraph is a set of three actors ni, nj and nk

where i



k [14]. While a triad could be without any

tie between its three constituent actors, there could be

links between the three actors of a triad. There are ex-

actly

 

gg*g1 *g26









triads for a network of size g.

As three actors constitute a triad and each actor can

relate to other two actors, there are six possible ties or

links between actors. In the mathematical presentation of

a triad, each of these six arcs can be present or absent.

Thus, there are 26 = 64 realizations or possible states for

a triad if node labels are considered. However, some of

these 64 states are isomorphic or structurally indistin-

guishable if node labels are ignored. There are sixteen

isomorphism classes for the 64 different triad states [16],

which are pictured in Figure 2. The triad census consists

of these sixteen isomorphism categories. Like dyad cen-

sus, models based on triad census can be used to test the

presence of configurable biases such as transitivity bias.

4. Research Dataset

This study considers Enron email communication data of

the year 2001 as the data for crisis communication net-

work. In order to fully understand the context of this re-

search, it is required to understand Enron’s organiza-

tional downfall, mostly instigated by the unethical busi-

ness practices of its senior management and overall or-

ganizational culture. Enron was founded in 1985 through

the merger of two gas pipeline companies. Within a de-

cade, this organization became a global player and a

symbol of innovative and progressive business conglo-

merate. It also became actively involved in the area of

metals, pulps and paper, broadband assets, water plants

and financial markets internationally [17]. In the year

2000, Enron’s annual revenue was $101 billion which

made it the seventh largest company in the United States,

bigger than IBM or Sony [18]. On mysterious circum-

stances, Jeff Skilling resigned as CEO on August 14,

1-003 2-012 3-102 4-021D

5-021U 6-021C 7-111D 8-111U

9-030T 10-030C 11-201 12-120D

13-120U 14-120C 15-210 16-300

Figure 2. Sixteen triad isomorphism classes. The number

(i.e. 1 to 16) before the hyphen for each labeling represents

the triad ID number. The characters after the hyphen fol-

low standard M-A-N labeling convention: the first charac-

ter gives the number of mutual dyads, the second character

gives the number of asymmetric dyads, the third character

gives the number of null dyads and the last character (if

present) is used to distinguish further among the types of

classes (“D” for down, “U” for up, “T” for transitive and

“C” for cycle).

S. UDDIN, L. HOSSAIN 35

2001 and was replaced by Kenneth Lay, the founder.

During the same month, it became slowly evident that,

with the help of Arthur Andersen (Enron’s auditor since

1985), Enron had been grossly overstating its profits and

understating debts for the previous 5 years. On October

16, 2001, Enron disclosed that it had lost $618 million in

the third quarter earnings. On December 2, 2001, Enron

filed for chapter 11 bankruptcy protection in a New York

Bankruptcy court. With $62 billion in assets, this was the

largest bankruptcy in the history of the US up to that time.

By January 2002, Enron stock lost 99% of its value.

Stockholders lost tens of billions of dollars and many of

the company’s 20,000 employees lost their retirement

saving pensions and jobs [17-19]. Since filing for bank-

ruptcy on December 2, 2001, the Justice Department

conducted an ongoing criminal investigation into the fall

of Enron. This investigation had been resulted in a num-

ber of criminal charges including fraud, conspiracy and

insider trading being filed against several top executives.

In May 2002, the US Federal Energy Regulatory

Commission (FERC) publicly released a large set of

email messages, the Enron corpus. The corpus contains

619,446 email messages belonging to 158 users over a

period of 3.5 years. Shetty and Adibi [20] of University

of Southern California created a MySQL database of this

corpus. They also cleaned the database by removing a

large number of duplicate emails, computer generated

folders, junk data, invalid email addresses and blank

messages. The resulting dataset contains 252,759 mes-

sages from 151 employees distributed in and around

3000 user defined folders. In this study, this database is

utilized to perform required data experiment. In the area

of organizational science and social network research,

the Enron corpus is of great value because it allows aca-

demics to conduct research on real-life organization over

a number of years.

Even though Enron email corpus has the email com-

munication data prior to and after the year 2001, this

study considers data only for the year 2001 since Enron’s

organizational crisis was at its peak during this period

which resulted in the bankruptcy declaration during the

first week of December 2001. UCINET [21] and Pajek

software were used for triad census analysis; whereas, for

dyad census analysis (i.e. Katz and Powell Index) vari-

ables of Equation (5) are first measured in Microsoft Ex-

cel. After that, kp is calculated from these variables.

5. Result

This section presents results of dyad and triad census

analysis of the crisis communication network of Enron.

These results highlight the presence of different struc-

tural patterns of communication structure associated with

the last stage of organizational crisis of Enron.

5.1. Dyad Census

The number of dyads in the communication network for

each of 52 weeks of the year 2001 is first identified. The

result is presented in Figure 3. Then, values for kp of

the same time period (i.e. 52 weeks of the year 2001) are

compared in Fi g u r e 4.

From Figures 3 and 4, it is evident that there is a sharp

increase in the number of dyads and in the value of kp as

the Enron’s crisis communication network moves to-

wards the peak crisis period. Although the trend is not

monotonically increasing, the dramatic increase in dyads

and kp during September, October and November of

2001 are significant.

5.1.1. Testing for the Distribution of Data Generation

Dyadic statistics are used to test hypothesis related to the

process of data generation for weekly networks under

study. It is assumed that weekly networks are distributed

as uniform random directed graph. This assumption im-

plies that elements of the sociomatrix X for each weekly

network are independent and have a constant probability

of 0.5 of being unity. That is, each element of X is a

Bernoulli random variable. Let, L is equal to the count of

how many of these Bernoulli random variables are unity.

Since, the sum of independent Bernoulli variables, with

constant probability P of being unity, is a Binomial ran-

dom variable with parameters equal to number of Ber-

noulli random variables being summed (i.e. g (g − 1)) and

Number of Dyad over Time

100

150

200

250

300

051015 20 25 30 35 4045 50 55

We e ks of the ye ar 2001

Number of Dya

Figure 3. Change of the number of dyads in 52 different

weeks of the year 2001 for the crisis communication net-

work of Enron.

Change of Katz-Powell Index

0.00

0.05

0.10

0.15

0.20

0.25

0.30

051015 20 25 30 35 40 45 50 55

Weeks of the year 2001

KP Inde

Figure 4. Change of kp in 52 different weeks of 2001 for the

crisis communication network of Enron.

S. UDDIN, L. HOSSAIN

the probability that any one of the variables is unity (i.e.

P), the null hypothesis for L can be represented by the

following equation [14]:







H:~Bingg 1,0.5L (6)

As the value for g is high for the Enron email corpus,

L should be approximately Gaussian or normally distri-

buted. Thus, the proposed null hypothesis can be tested

by the following test statistics [14]:



lELlgg

zVar Lgg





 (7)

where,

i1j1





is the number of links actually observed in the weekly

directed network under study.

Although there is a sharp increase in the zl values dur-

ing the crisis period, as pictured in Figure 5, it is noticed

that the corresponding p values for all weekly networks

are nearly 0. Thus, it is highly unlikely that the data for

Enron email corpus have been generated by the Bernoulli

process. Therefore, it can be concluded that there is no

significant difference in the data generation process dur-

ing crisis and non-crisis period for Enron.

5.2. Triad Census

Using UCINET and Pajek, frequencies of sixteen iso-

morphism classes of triad census are measured for each

of 52 weeks of the year 2001 of the Enron communica-

tion network. As the first three isomorphism classes of

triad census (i.e. the first three structures in Figure 2) are

considered in the dyad census analysis, the triad census

analysis is represented in the Figure 6 by considering the

rest thirteen isomorphism structures. The triad census

analysis in Pajek produces both frequencies of isomor-

phism classes and their expected numbers. Before pre-

senting the triad census output, it is required to normalize

frequencies of triadic isomorphism classes for each

weekly network by subtracting the expected number of

Z statistics for different week ly network s

200

400

600

800

1000

1200

1400

1600

1800

04812 16 20 2428 32 36 40 44 48 52

Weeks of the year 2001

z valu

Figure 5. Zl statistics for 52 weekly networks of 2001 of the

crisis communication network of Enron.

the frequency from the real number of frequency for each

isomorphism class. In Figure 6(a), results of those struc-

tures for which there is an absent link between actors (i.e.

structures 4 to structure 8 and structure 11 in the Figure

2) are presented. The results for the rest isomorphism

classes (i.e. structure 9 to structure 16 except structure 11

in Figure 2) are illustrated in Fi gu r e 6 (b).

From Figures 6(a) and (b), it is revealed that there is a

sharp increase in frequencies during the crisis period of

Enron for all isomorphism classes. In some cases, as like

for the isomorphism class of 16 - 300, evidences of high

frequencies are noticed during the normal time (i.e. non-

crisis period).

5.2.1. Testing for Transitivity

The results from the triad census analysis are further

utilized to test the structural hypothesis about transitivity.

Three actors (say A, B and C) are transitive if whenever

A is linked to B and B is linked to C, then A is also linked

to C. The three actors of any transitive structure are re-

ferred as a transitive triple [22].

Out of thirteen isomorphism classes for triad census,

as pictured in Figure 4, six of them have at least one

transitivity configuration. They are illustrated in Figure

7. Isomorphism class 9 - 030 T and 14 - 210 have one

transitive triple; 12 - 120 D and 13 - 120 U have two

transitive triples; 15 - 210 has three transitive triples; and

16 - 300 has six transitive triples. In order to count the

total transitive configurations for a given directed net-

work from its triad census, it is required to multiply the

frequency of each isomorphism class by the number of

transitive triple(s) being presented in its structure. For

instance, if frequencies of 9 - 030 T, 12 - 120 D, 13 - 120

U, 14 - 210, 15 - 210 and 16 - 300 are 1, 2, 3, 4, 5 and 6

respectively then the total number of transitive configu-

ration will be 66 (i.e. 1  1 + 2  2 + 3  2 + 4  1 + 5 

3 + 6  6 = 66). Frequencies of transitive configurations

for 52 weeks of the year 2001 of Enron email corpus are

compared in Figure 8.

From the Figure 8, it is evident that there is a signifi-

cant increase in the number of transitive configurations

in the Enron email corpus during the crisis period. There-

fore, it can be concluded that crisis communication net-

work of an organization becomes increasingly transitive

as that organization experiences crisis.

6. Discussion

Dyad and triad census analysis have been conducted to

explore 52 weekly networks of Enron email communica-

tion network. Both dyad and triad census output revealed

notable changes in the pattern of communication struc-

ture during the organizational crisis period. More spe-

cifically, they showed significant increases in the occur-

rences of different micro-level structural patterns in

S. UDDIN, L. HOSSAIN

communication network as Enron approached disintegra-

tion during the last quarter of 2001. It is important to

note that this was the time during which Enron was in

complete turmoil. Jeff Skilling resigned as CEO on Au-

gust, 14 2001. After some time, during mid October, the

company announced that it had lost $618 million dollars

in the third quarter earnings which eventually lead to the

bankruptcy declaration on December 2, 2001. The bank-

ruptcy declaration and subsequent departure of many

employees lead to the reduction in the number of differ-

ent micro-level dyad and triad census structures in late

December.

exchanges of resources, both psychological and econo-

mical, between each member of the dyad. Social Support

Theory, which was proposed by Kadushin and Kadushin

[25] and Lin and Ensel [26], can explain theoretical

mechanisms behind actors’ getting closer in the commu-

nication network during crisis. According to this theory,

intimate relations with others whom one might confide

and receive various forms of feedback may significantly

affect one’s well-being. Murshed et al. [27] described

how the formation of transitive structure, which has a

striking resemblance to the concept of Balance Theory,

in the communication network during crisis can be ex-

plained by the Balanced Theory. They argued that people

prefer balanced structure in their day to day lives. If the

structure is not balanced then people experience various

psychological effects such as “strain” and “tension”. As

organizations go through the state of crisis, people also

experience “strain” and “stresses”, which will ultimately

lead actors to form a balanced state within the communi-

cation structure.

The high frequencies of micro-level structures for

dyad and triad census indicate that actors or individuals

within Enron had come closer during its crisis period.

The reason behind this can be explained by several ex-

isting theories. For example, Social Exchange Theory

can be employed to vindicate the reciprocity within com-

munication network. This theory, which was originally

introduced by Homans [23,24] seeks to explain the like-

lihood of a reciprocal or dyadic relationship based on the There are also many well-known and highly cited

I somorphism class: 4-021D

10000

20000

30000

40000

50000

60000

048 1216202428323640444852

W ee k s of th e year 2001

Number of 021D

Isomorp hism class: 5-021U

100

200

300

400

500

048 1216202428323640444852

Weeks of the year 2001

Number of 021U

Isomorphi sm class: 6-021C

500

1000

1500

2000

048 1216202428323640444852

W ee ks of th e ye ar 2001

Number of 021C

Isomorphism class: 7-111D

100

120

048 1216202428323640444852

W eeks of the ye ar 2001

Number of 111D

Isomorphi sm class: 8-111U

500

1000

1500

2000

2500

3000

3500

04812 1620 24283236 40 4448 52

W eeks of the year 2001

Number of 111U

Isomorphi sm class: 11-201

04812 16 20 24 28 32 36 40 44 48 52

Weeks of the year 2001

Number of 201

(a)

S. UDDIN, L. HOSSAIN

I somorp hism class: 9-030T

100

150

200

04812 16 20 2428 32 36 4044 48 52

W ee k s of the ye ar 2001

Number of 030T

Isomorphism class: 10-030C

0481216 20242832 364044 48 52

W ee k s of the ye ar 2001

Number of 030C

Isomor phism cl ass: 12-120D

048 1216202428323640444852

Weeks of th e year 2001

Number of 120D

Isomor phism c l ass: 13-120U

100

200

300

400

048 1216202428323640444852

W ee k s of the year 2001

Number of 120U

Isomorphi sm class: 14-120C

0481216202428323640444852

W ee k s of the ye ar 2001

Number of 120C

I somorphism class: 15-210

048 1216202428323640444852

W ee ks of the ye ar 2001

Number of 210

I som orphism class: 16-300

0481216202428323640444852

We eks of the ye ar 2001

Number of 300

(b)

Figure 6. (a) Changes of the number of different isomorphism structures for which there is an absent link between actors (i.e .

structure 4 to structure 8 and structure 11) over the 52 weeks of the year 2001 of the crisis communication network of Enron;

(b). Change of the number of different isomorphism structures having at lest one link between actors (i.e. structure 9 to

structure 16 except structure 11) over the 52 weeks of the year 2001 of crisis communication network of Enron.

S. UDDIN, L. HOSSAIN

12-120D

13-120U

9-030T 14-120C

15-210

16-300

1 transitive triple

2 transitive triples

1 transitive triple

2 intransitive triples

3 transitive triples

1 intransitive triple

6 transitive triples

Figure 7. Statistics about transitive and intransitive triples for each of the six transitive configurations that are members of

sixteen triadic isomorphism classes.

Frequency of transitive configurations

200

400

600

800

1000

048 1216202428323640444852

Weeks of the year 2001

# tr ansitive c onfigu r ation

Figure 8. Frequencies of transitive configurations for 52 weeks of the year 2001 for crisis communication network of Enron.

studies in the current literature which argued that during

crisis: there is a decrease in interpersonal friction and an

increase in collaboration [28]; an increased cohesion

among actors and a high possibility to bring people to-

gether who would otherwise have nothing to do each

other [29]; an increased tendency of seeking company of

others [30]; and a reduction in inter-group conflicts [31].

All of these impacts of crisis bring actors or individuals

within organizations closer, which eventually make the

communication network among them denser as noticed

from the dyad and triad census analysis for Enron email

communication data in this study.

7. Conclusion

This paper compares frequencies of different micro-level

structures of dyad and triad census for Enron email com-

munication network of the year 2001. Associations of

different patterns with the Enron crisis period, which

mainly started at the beginning of last quarter of 2001,

have been noticed in this study. In dyad census analysis,

higher value for Katz and Powell Index for Mutuality is

observed during the organizational crisis period. Higher

frequencies of different isomorphism classes of triad

census are noticed during the Enron crisis period. Hy-

potheses related to data generation process and tendency

to certain structural patterns are further tested using re-

sults from dyad and triad census analysis respectively.

The methodological contribution of this study is wor-

thy of note. This study utilizes dyad and triad census

analysis to explore crisis communication network of or-

S. UDDIN, L. HOSSAIN

ganizations. With the increasing popularity of email as an

interaction medium and increased popularity of social

network analysis methods and tools, it is expected that a

deeper understanding of the various social and organiza-

tional phenomena using further concepts of dyad and

triad census analysis such as subgraph analysis, distribu-

tion analysis and stability analysis could be developed.

Unlike studies that explored structural behaviors such as

power-law behavior [32] and longitudinal topology of

network dynamics [33,34], this study explores the pre-

sence of different micro-structures of dyad and triad cen-

sus analysis for a crisis communication network.

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