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The Artificial Immune Systems Domain: Identifying Progress and Main Contributors Using Publication and Co-Authorship Analyses A Abi Haidar, A Six, JG Ganascia, V Thomas-Vaslin Advances in Artificial Life, ECAL 12, 1206-1217


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Abstract Much can be learned about the progress, fathers and future of a scientific domain from the analysis of a collection of relevant articles and their corresponding authors. Here, we study the highly interdisciplinary domain of Artificial Immune System (AIS) since its birth, a couple of decades ago. We apply Social Network Analysis to the coauthorship network of the most comprehensive publicly accessible AIS bibliography. We automatically extract publication dates and author names from the bibliography and evaluate authors with the ...
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The Artificial Immune Systems Domain:
Identifying Progress and Main Contributors
Using Publication and Co-Authorship Analyses
Alaa ABI HAIDAR1,2,3,4, Adrien SIX1,2, Veronique THOMAS-VASLIN1,2, and
Jean-Gabriel GANASCIA3,4
1CNRS, UMR 7211, Immunology, Immunopathology, Immunotherapy, 75013 Paris
2UPMC Univ Paris 06, UMR 7211, Integrative Immunology team, 75013 Paris
3CNRS, UMR 7606, ACASA, LIP6, 4 place Jussieu, 75005 Paris
4UPMC Univ Paris 06, UMR 7606, ACASA . LIP6 . 4 place Jussieu, 75005 Paris
Abstract. Much can be learned about the progress, fathers and fu-
ture of a scientific domain from the analysis of a collection of relevant
articles and their corresponding authors. Here, we study the highly in-
terdisciplinary domain of Artificial Immune System (AIS) since its birth,
a couple of decades ago. We apply Social Network Analysis to the co-
authorship network of the most comprehensive publicly accessible AIS
bibliography. We automatically extract publication dates and author
names from the bibliography and evaluate authors with the highest de-
gree (unique collaborations) and centrality (influence).
Our results highlight the relative growth of publication volume and iden-
tify significant contributors in the AIS field. Furthermore, our findings
are not only encouraging for the AIS community but may be useful for
analyses of other scientific communities and leading contributors therein.
Keywords: Artificial Immune Systems, Social Network Analysis, Co-
authorship, Information Extraction, Text Mining
1 Introduction
Artificial Immune Systems (AIS) are adaptive systems, inspired by theories and
observed principles of the immune system, and applied towards solving compu-
tational problems [1]. Common AIS techniques are based on specific theoreti-
cal models explaining the behavior of the vertebrate adaptive immune system
such as negative selection, clonal selection, immune networks and dendritic cells
[2]. AIS are mainly classified into two categories: The first one aims at mathe-
matically modeling the immune system to better understand its behavior. The
second one uses the immune system as a metaphorical inspiration to engineer
algorithms that are capable of learning and solving a huge variety of machine
learning problems such as classification, clustering and regression analysis.
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AIS is a relatively new field which began in the mid 80’s with the modeling
and refinement of Jerne’s immune network theory [3] by Farmer et al. [4], and
later by Bersini and Varela [5, 6]. However, it wasn’t until negative selection was
used by Forrest [7] for protecting computer networks from viruses that the AIS
domain was established. Cooke and Hunt [8,9] adapted immune networks for
classification and Timmis [10] further improved it while De Castro et al [11, 12]
worked on aiNet for multimodal function optimization and data analysis. The
first book on AIS was edited by Dasgupta in 1998 [13].
In the past few years, several review papers have discussed the slow advances
in AIS and proposed improvement strategies through novel and simpler AIS
models (inspired by the vertebrate innate immune systems and immune systems
of plants) as well as the development of a unified architecture for integration of
existing models [14, 2,15, 16]. Timmis argues that AIS has reached an impasse [2]
and Timmis et al pointed out a dearth of theory to justify the use and continuity
of AIS [17] .
The domain of AIS has existed for a couple of decades but has never been
analyzed quantitatively. Furthermore, the fear that the domain is stagnating was
only based on qualitative studies targeting specific AIS models and frameworks,
such as negative selection, clonal selection and more recently, the danger theory.
In this paper we use techniques from co-authorship network analysis and
statistical methods in order to investigate the current state and future of the
domain of AIS. Moreover, we identify leading contributors to the field using co-
authorship network analysis and discuss our results. In the following section, we
discuss the methods used for information extraction and statistical analysis, in
addition to social network analysis of the co-authorship network. In section 3,
we illustrate and discuss the growth of the AIS domain and compare it to the
general growth of Medline indexed articles in general. Finally, in section 3.3, we
discuss our results and the future directions of the field.
2 Methods
We adopt data mining techniques in order to extract author names and publi-
cation dates from the most comprehensive AIS bibliography [18] compromising
1044 articles and 994 authors. We only had access to a binary PDF format of the
bibliography and therefore we had to convert it into an ASCII textual format.
The converted text was far from the desired clean structured text and therefore
a significant amount of manual curation was involved in order to systematically
mark the end of the authors’ list before automating the extraction of author
Publication dates are limited to the year of publication consisting of a 4-digit
number YYYY where 1980 < YYYY < 2010 in order to avoid confusion with
other numbers such as digital object identifiers (doi), volume numbers and serial
numbers. More challenging was the task of author name extraction with some
authors having their first names abbreviated others having full first names and
others not respecting the first-name-last-name order. We manually restructured
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The Artificial Immune Systems Domain 3
the bibliography, ensuring that last names were preceded by abbreviated or full
names. We prepended shared last names with the abbreviated first names to
avoid the risk of agglomerating authors with a common last name. For example
“X Lee” and “Y Lee” are considered two distinct authors and represented as:
X-Lee and Y-Lee, respectively. Still, we face the possibility that two different
authors would be counted as one if they share both lastnames and firstnames
(at least initial). However, that is outside the scope of our analysis.
2.1 Social Network Analysis
Social Network Analysis (SNA) has played major roles in many disciplines in
the past few years [19, 20]. Co-authorship Networks (CN) are social networks
consisting of scientific collaborations and collaborators [21]. In CN, the authors
are represented as nodes (or vertices) and collaborations as undirected edges. CN
are similar to citation networks in the scientific literature [22]. However, CN have
better social and collaborative implications [23]. CN Analysis has already been
studied and applied to a couple of fields but never to AIS [23–25]. Indeed, studies
on co-authorship analysis have shown how visible and influential can article be
[26]. Other studies have focused on examining academic research performance
based on a co-authorship network of centrality and gender [27].
We use several existing methods to analyze a CN of AIS as follows:
We used the R package [28] to calculate the degree, closeness, and between-
ness centrality for the binary undirected co-authorship network. In the following
sections, we illustrate and discuss the 20 highest ranking authors for each of the
following metrics:
Degree is a measure of the unique number of collaborators an author has.
Closeness, that is only applied to the largest (connected) component, is a
measure of how authors are directly connected to a well-connected author.
Betweenness is a measure of a node’s influence for information flow in the
network. Betweenness measures how many times a node is visited when two
random nodes are connected through a path of nodes.
For more information about centrality measures and SNA, please refer to
2.2 PageRank
PageRank [29], or eigenvector centrality, is used by the Google search engine to
determine a page’s relevance or importance. Important pages receive a higher
PageRank and are more likely to appear at the top of the search results. PageR-
ank is based on backlinks. The more quality backlinks the higher google pager-
Liu et al [23] have applied PageRank to a co-authorship network of the Digital
Science domain in order to identify prestigious authors. They transformed each
undirectional edge into a set of bi-directional, symmetrical edges.
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2.3 AuthorRank
Liu et al [23] also define a modification of PageRank, that they call AuthorRank.
PageRank assumes that when a node Aconnects to nother nodes, it receives a
weight based on an equal fraction 1
n, whereas AuthorRank attributes different
weights for each author based on the number of their publications in common
Both PageRank and AuthorRank are measures of prestige that we use to
identify leading authors in the domain of AIS as discussed in the following sec-
We used the R package [28] to implement PageRank and AuthorRank to
rank the top ranking authors and to visualize the co-authorship network using
a Fruchterman Reingold Layout.
3 Results and Discussion
3.1 Publication Distribution Analysis
In order to answer the numerous doubts about a decline in the AIS field, we
have measured the number of publications that are relevant to the field over
time. Furthermore, we have fitted our observation using exponential functions
to study the growth of publication size over time and to predict it over the years
to come. We have used the coefficient of determination R2in order to validate
the fitness function and its prediction as shown in figure 1.
Fig. 1. Number of Publications per year 1) according to the AIS bibliography [blue], 2)
according to google scholar results for “Artificial Immune Systems” [green] and 3) for
all indexed Pubmed articles [red] per year. The results show a relative growth in the
domain based on both AIS bibliography and Google Scholar results. The exponential
fits are validated using the R2coefficient.
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Contrary to previous fears of a stagnating AIS field, we have shown that the
field of AIS is ever growing by measuring the number of publications over time.
Indeed, we have used exponential fitness functions in the form of f(x)=c.e(x.b),
where cis a constant, xis the year index and bis the growth factor. We have
compared the growth in the volume of AIS articles according to our AIS bib-
liography (b=0.2) to that of indexed Pubmed articles (b=0.03) between the
years of 1984 and 20081using Corlan’s Medline trend (http://dan.corlan.
net/medline-trend.html) to cement our conclusion regarding the relative ex-
pansion of the AIS domain. Furthermore, we add to our perspective the number
of articles returned by Google Scholar for the query ”Artificial Immune Systems”
which results in a faster growing trend (b=0.4) as shown in green in figure 1.
Dasgupta’s Bibliography includes conference proceedings, as can be found in
Google Scholar, whereas PubMed indexes journal articles only. However, we ar-
gue that in the fields of informatics and engineering, conference and workshop
publications typically have higher impact
Fig. 2. The distribution of publications per authors forms a gaussian distribution
around two authors.
In addition, we have analyzed the number of authors per publication that is
an indication of collaboration strength. As shown in figure 2, most collaborations
include two authors. Moreover, there are more 3 co-authored publications than
(and almost as many 4 co-authored publications as) single authored ones. We
presume that this may be as a result of the field of AIS being a very collaborative
one. Indeed, a highly interdisciplinary field such as AIS invites collaborations
from the fields of immunology, systems biology, artificial intelligence, machine
learning, data mining...etc. Similar studies have been conducted on the field of
the Digital Library Research Community [23] yielding similar results with 28.5%
of papers authored by 2 authors, 23.6% by 3 authors, 19.6% by a single author
and 9.4% by 4 authors.
1The years covered in the AIS Bibliography
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3.2 Co-Authorship Network Analysis
The co-authorship network is summarized in table 1 and visualized in figure 3.
Network Statistics
Articles 1044
Number of Vertices 994
Number of Edges 1768
Network Density 0.0036
Number of Clusters 159
Largest Component Size 55%
Table 1. Co-Authorship network summary
The number of publications per author may be a good indicator of an author’s
contribution to the field however that can be biased in favor of non-collaborative
authors with many published articles. The author’s degree, or number of unique
collaborators, can be a better measure of an author’s collaborative efforts in a
field. Degree centrality measures authors’ connectivity with immediate neigh-
bors or collaborators. Some authors may, however, be locally well connected but
not globally with the entire network. Closeness centrality expands on degree
centrality and favors authors that are connected (directly or indirectly) to as
many authors in the network. Betweenness is another measure of centrality that
measures how often a node is on a shortest path between any two random nodes
in the network. Betweenness conveys the role of an author as an information
spreader or a hub. The authors with the highest number of publications, degree,
betweenness and closeness are listed in table 2. Authors with the highest degree,
betweenness and closeness measures are illustrated in figure 4.
Several studies have analysed Co-authorship Network (CN) components for
various scientific domains. Nascimento [24] reports the largest component in
SIGMODs2CN having about 60% of all authors. Newman [25] has studied sev-
eral CN with the smallest “largest component”, containing 57.2% of all authors.
Liu et al [23] report in the JCDL3CN the largest component of only 38% (599
authors) of all authors for the years between 1994 and 2003. The low percentage
may be due to a relative immaturity of the Digital Library (DL) field. The AIS
CN has the largest component of 55% (550 authors) of all authors, thus showing
a relative maturity of the field. We suspect that the maturity of AIS is related
to 10 years of the dedicated conference, ICARIS, since 2002 and 20 years since
the beginning of AIS, i.e. twice as longer than the DL field. The largest compo-
nent of 550 authors is visualized in black at the center of figure 3, whereas the
remaining components alternate in various colors around it. Moreover, table 3
2SIGMOD is a Special Interest Group on Management Of Data under the Association
of Machinery, ACM
3Joint Conference on Digital Library
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Fig. 3. A visualization of the AIS co-authorship network with nodes representing au-
thors and edges representing collaborations (with at least one co-authored article). Each
component is represented in a different color. In particular, the largest component (in
black) contains 55% of all authors.
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Rank Author Publications Author Degree Author Betweenness Author Closeness
1Timmis 83 Timmis 50 Timmis 86260 Timmis 0.22405
2Dasgupta 71 Forrest 35 Dasgupta 35531 R-Smith 0.22391
3Forrest 68 Castro 29 Forrest 20535 Bentley 0.22390
4Castro 58 Dasgupta 28 Castro 18859 Nicosia 0.22389
5Zuben 42 Perelson 28 X-Wang 17737 Tyrrell 0.22389
6Aickelin 38 X-Wang 24 A-Freitas 17628 Chow 0.22386
7Perelson 35 D-Lee 22 Chow 16984 Y-Liu 0.22386
8Hart 20 Zuben 20 Hart 16183 Clark 0.22385
9Gonzalez 19 J-Kim 18 R-Smith 15714 Neal 0.22385
10 Stibor 18 Lamont 18 Tyrrell 15451 Lau 0.22384
11 Neal 17 Aickelin 17 Y-Liu 14936 Forrest 0.22384
12 Hunt 17 Tarakanov 16 Bentley 14827 Cutello 0.22384
13 Bersini 17 A-Freitas 15 Perelson 13960 Pavone 0.22384
14 Lamont 16 Nicosia 15 Stewart 13312 Goncharova 0.22384
15 Lau 16 Faro 15 Aickelin 12671 Knight 0.22384
16 Esponda 15 Bentley 13 Gao 12462 Stepney 0.22384
17 J-Kim 15 Oliveira 13 Carvalho 11618 Dasgupta 0.22383
18 Bentley 15 Hart 13 Jackson 11594 Castro 0.22383
19 Greensmith 15 Clark 12 Huang 11481 Hart 0.22383
20 Tarakanov 15 Gonzalez 12 Bersini 11119 A-Freitas 0.22382
Table 2. AIS authors ranked according to their number of publications, degree (or
number of unique collaborators), betweenness and closeness.
Component Size 1 2 3 4 5 6 7 8 9 10 11 12 15 550
Number of Components 39 60 22 15 9 4 1 2 1 2 1 1 1 1
Table 3. AIS Co-authorship Network component sizes and frequencies with the largest
component boldened
lists the component sizes and frequencies of the AIS CN showing a significant
amount of smaller components. This is suggestive of many existing AIS sub-
communities, that collaborate individually but can eventually collaborate with
other sub-communities .
Table 2 distinguishes Jon Timmis as a top contributor and collaborator in
this field, however, we are also interested in identifying other contributors in
the AIS domain like the founding fathers such as Forrest and De Castro. In
table 2, we identify authors with significantly high centrality (top 20 in degree
and either betweenness or closeness4) such as Nicosia, A.Freitas in the AIS CN
regardless of their relatively lower number of publications (not in the top 20
in number of publications). Conversely, authors such as Neal and Stibor rank
amongst the top 20 for their number of publications but do not rank as highly
4We look at the betweenness and closeness together given the minute variation in the
top ranked authors according to closeness centrality shown in table 2
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with the centrality measures. We assume this is due to their collaboration with
a fewer selection of authors. Nevertheless, Neal and Stibor reappear in the top
ranking authors according to AuthorRank in table 4 for their collaborations with
few but “prestigious” authors (in terms of AuthorRank) such as Timmis, Hunt
and Eckert.
PageRank and AuthorRank are measures of prestige. An author ranks highly
if he or she collaborate with many authors that, in turn, have many collaborators
ad libitum. AuthorRank gives more weight to repeated collaborations (in a
weighted network), whereas PageRank distributes weights evenly (in a boolean
network). The top 20 authors according to PageRank and AuthorRank are listed
in table 4.
Rank PageRank AuthorRank
1Timmis Timmis
2 Forrest Castro
3Dasgupta Neal
4 Perelson Stibor
5 Castro Hunt
6X-Wang Knight
7 D-Lee Eckert
8 Zuben Zuben
9Lamont A-Freitas
10 Aickelin Watkins
11 J-Kim Andrews
12 Coello Clark
13 Tarakanov Hart
14 Hart Ayara
15 Nicosia Lemos
16 Chowdhury Mohr
17 Fukuda Secker
18 Yang Owens
19 Gonzalez Hone
20 J-Zhang Kelsey
Table 4. Authors ranked according to the metrics of PageRank and AuthorRank.
We are equally interested in identifying contributors to the field with lead-
ing AIS theoretical models, such as Jerne, Matzinger, Perelson, Bersini, Varela,
Carneiro and Greensmith, recurrent committee members of the International
Conference of Artificial Immune Systems (ICARIS) such as Timmis, Forrest
and Nicosa, as well as principle investigators or team leaders in AIS, such as
Hart, Timmis, Von Zuben and D. Dasgupta. Many of these names appear re-
peatedly in table 2 of top ranking authors according to centrality measures, and
in table 4 of top ranking authors according to PageRank and AuthorRank.
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Finally, we are interested in understanding large network components that
are disconnected from the largest component such as those led by Fukuda, Kara,
Watanabe and Coello. We presume that separation is due to language and geo-
graphic barriers but we hope to have a more integrated network or more method-
ological explanations about this segragation in the near future. We are as well
interested in understanding cluster propoerties of the largest component mainly
led by team leaders, namely, Forrest, Timmis, Hart, Dasgupta and Tarakanov.
Fig. 4. Alphabetically sorted list of authors with the highest degree, betweenness and
3.3 Conclusion and Future Directions
Several reviews have discussed advances in the field of Artificial Immune Sys-
tems but all from a qualitative perspective. Recent reviews have alluded to a
stagnation in the field of AIS. In this work, we investigated these questions from
a quantitative perspective. Our results have shown that the field has been grow-
ing ever since it was established for the past couple of decades. In addition, we
have identified leading contributors by co-authorship network analysis based on
AIS-relevant publications.
We acknowledge that the bibliography may be biased towards an engineering
perspective as it is maintained by a computer scientist thus dismissing funda-
mental contribution from the theoretical immunology perspective. However, our
method can be applied to any bibliography (structured or unstructured) in any
scientific domain. Hence, we expect our analytical method not only to motivate
the AIS community and encourage external scientists to entertain the challenges
presented by AIS, but also to be a benchmark for scientific domain analyses.
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Acknowledgments. This research has been supported by the DIM ISC 2011
Grant ”Probl´ematiques transversales aux syst`emes complexes” Region Paris Ile-
de-France through the Institue of Complex Systems (ISC-PIF). We acknowledge
the help of Hugues RIPOCHE with the extraction of dates and the insights
brought by Luis M. ROCHA and Chris MCEWAN.
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The visibility of an article depends to a large extent on its authors. We study the question how each co-author’s relative contribution to the visibility of the article can be determined and quantified using an indicator, referring to such an indicator as a CAV-indicator. A two-step procedure is elaborated, whereby one first chooses an indicator (e.g. total number of citations, h-index …) and subsequently one of two possible approaches. The case where the indicator is an h-type index is elaborated in a Lotkaian framework. Different examples illustrate the procedure and the choices involved in determining a CAV-indicator.
The complexity and variety of bibliographic data is growing, and efforts to define new methodologies and techniques for bibliometric analysis are intensifying. In this complex scenario, one of the most crucial issues is the quality of data and the capability of bibliometric analysis to cope with multiple data dimensions. Although the problem of enforcing a multidimensional approach to the analysis and management of bibliographic data is not new, a reference design pattern and a specific conceptual model for multidimensional analysis of bibliographic data are still missing. In this paper, we discuss ten of the most relevant challenges for bibliometric analysis when dealing with multidimensional data, and we propose a reference data model that, according to different goals, can help analysis designers and bibliographic experts in working with large collections of bibliographic data.
In this paper we describe an artificial immune system (AIS) which is based upon models of the natural immune system. This natural system is an example of an evolutionary learning mechanism which possesses a content addressable memory and the ability to «forget» little-used information. It is also an example of an adaptive non-linear network in which control is decentralized and problem processing is efficient and effective. As such, the immune system has the potential to offer novel problem solving methods. The AIS is an example of a system developed around the current understanding of the immune system. It illustrates how an artificial immune system can capture the basic elements of the immune system and exhibit some of its chief characteristics. We illustrate the potential of the AIS on a simple pattern recognition problem. We then apply the AIS to a real-world problem: the recognition of promoters in DNA sequences. The results obtained are consistent with other appproaches, such as neural networks and Quinlan's ID3 and are better than the nearest neighbour algorithm. The primary advantages of the AIS are that it only requires positive examples, and the patterns it has learnt can be explicitly examined. In addition, because it is self-organizing, it does not require effort to optimize any system parameters.
This is a pioneering work on the emerging field of artificial immune systems-highly distributed systems based on the principles of the natural system. Like artificial neural networks, artificial immune systems can learn new information and recall previously learned information. This book provides an overview of artificial immune systems, explaining its applications in areas such as immunological memory, anomaly detection algorithms, and modeling the effects of prior infection on vaccine efficacy.
Biology gives us numerous examples of self-assertional systems whose essence does not precede their existence but is rather revealed through it. Immune system is one of them. The fact of behaving in order not only to satisfy external constraints as a pre-fixed set of possible environments and objectives, but also to satisfy internal "viability" constraints justifies a sharper focus. Adaptability, creativity and memory are certainly interesting "side-effects" of such a tendency for self-consistency. However in this paper, we adopted a largely pragmatic attitude attempting to find the best hybridizing between the biological lessons and the engineering needs. The great difficulty, also shared by neural net and GA users, remains the precise localisation of the frontier where the biological reality must give way to a directed design.