September 11th Reuters terror news
The Reuters terror news network was obtained from the CRA (Centering Resonance Analysis) networks produced by Steve Corman and Kevin Dooley at Arizona State University. The network is based on all the stories released during 66 consecutive days by the news agency Reuters concerning the September 11 attack on the U.S., beginning at 9:00 AM EST 9/11/01. The nodes of this network are important words (terms). There is an edge between two words iff they appear in the same utterance (for details see the paper \cite{CRA}). The weight of an edge is its frequency. The network has n = 13332 nodes (different words in the news) and m = 243447 edges, 50859 with value larger than 1. There are no loops in the network.
The Reuters terror news network was used as a case network for the Viszards visualization session on the Sunbelt XXII International Sunbelt Social Network Conference, New Orleans, USA, 13-17. February 2002.
We transformed the Pajek version of the network into the Ianus format used in TQ. To identify important terms we computed their aggregated frequencies and extracted the subnetwork of the 50 most frequently used (during 66 days) nodes. They are listed in the following table:
50 most frequent terms in the Terror news network.
n term ∑freq n term ∑freq 1 united_states 15000 26 terrorism 2212 2 attack 10348 27 day 2128 3 taliban 6266 28 week 2017 4 people 5286 29 worker 1983 5 afghanistan 5176 30 office 1967 6 bin_laden 4885 31 group 1966 7 new_york 4832 32 air 1962 8 pres_bush 4506 33 minister 1919 9 washington 4047 34 time 1898 10 official 3902 35 hijack 1884 11 anthrax 3563 36 strike 1818 12 military 3394 37 afghan 1775 13 plane 3078 38 flight 1775 14 world_trade_ctr 3006 39 tell 1746 15 security 2906 40 terrorist 1745 16 american 2825 41 airport 1741 17 country 2794 42 pakistan 1714 18 city 2689 43 tower 1685 19 war 2679 44 bomb 1674 20 tuesday 2635 45 new 1650 21 pentagon 2620 46 buildng 1634 22 force 2516 47 wednesday 1593 23 government 2380 48 nation 1589 24 leader 2375 49 police 1587 25 world 2213 50 foreign 1558
Trying to draw this subnetwork it turns out to be almost a complete graph. To obtain something readable we removed all temporal edges with a value smaller than 10. The corresponding underlying graph is presented in the following figure. The isolated nodes were removed.
September 11th. Subnetwork of the most frequently used terms.
For each of the 50 nodes we determined its temporal activity and drew it. By visual inspection we identified 6 typical activity patterns – types of terms. For all charts in the figure the displayed values are in the interval [0,200] - the largest activity value for the term Wednesday is larger than 200.
The primary terms are the terms with a very high frequency of appearance in
the first week after September 11th and smaller, slowly declining
values in the following period. The representative of this group
in the figure is hijack and other members are:
airport, american, attack, city, day, flight, nation,
New York, official, Pentagon, people, plane, police, president Bush, security,
tower, United States, Washington, world, World Trade center.
These are the terms describing the event.
The secondary terms are a reaction to the event. There are no big changes in their values. We identified three subgroups:
a) slowly declining represented with bin Laden
(country, foreign, government, military, minister, new,
Pakistan, tell, terrorism, terrorist, time, war, week);
b) stationary represented with
taliban (afghan, Afghanistan, force, group, leader); and
c) occasional with several peaks, represented with
bomb (air, building, office, strike, worker).
There are three special patterns - two periodic
Wednesday and Tuesday; and one episodic anthrax.
| hijack |  |
| bin Laden |  |
| taliban |  |
| bomb |  |
| Wednesday |  |
| anthrax |  |
Types of activity.
To consider in a measure of importance of the node u ∈ V also the node's position in the network we constructed the attraction coefficient att(u).
Let A = [ auv] be a network matrix of temporal quantities with positive real values. We define the node activity act(u) as (see Section~\ref{activ})
act(u) = act({u}, V\{u}) = ∑v∈V\{u} auv .
Then the attraction of the node u is defined as
att(u) = 1/Δ ∑v∈V\{u} avu / act(v) .
Note that the fraction auv / act(v) is measuring the proportion of the activity of the node v that is shared with the node u.
From 0 ≤ avu / act(v) ≤ 1 and deg(v)=0 ⇒ avu=0 it follows that
∑v∈V\{u} avu / act(v) ≤ deg(u) ≤ Δ
where Δ denotes the maximum degree. Therefore we have 0 ≤ att(u) ≤ 1, for all u∈V.
The maximum possible attraction value 1 is attained exactly for nodes: a) in an undirected network: that are the root of a star; b) in a directed network: that are the only out-neighbors of their in-neighbors – the root of a directed in-star.
We computed the temporal attraction and the corresponding aggregated attraction values for all the nodes in our network. We selected 30 nodes with the largest aggregated attraction values. They are listed in the following table:
30 most attractive terms in the Terror news network.
n term ∑att n term ∑att 1 united_states 12.216 16 war 2.758 2 taliban 7.096 17 force 2.596 3 attack 7.070 18 new_york 2.590 4 afghanistan 5.142 19 government 2.496 5 people 5.023 20 day 2.338 6 bin_laden 4.660 21 leader 2.305 7 anthrax 4.601 22 terrorism 2.202 8 pres_bush 4.374 23 time 2.182 9 country 3.317 24 group 2.072 10 washington 3.067 25 afghan 2.040 11 security 2.939 26 world 1.995 12 american 2.922 27 week 1.961 13 official 2.831 28 pakistan 1.943 14 city 2.798 29 letter 1.866 15 military 2.793 30 new 1.851
Again we visually explored them. In the following figure we present temporal attraction coefficients for the 6 selected terms. For all charts in the figure the displayed attraction values are in the interval [0,0.2].
| pres Bush |  |
| Pakistan |  |
| taliban |  |
| Kabul |  |
| bomb |  |
| anthrax |  |
Attraction patterns.
Comparing on the common terms (taliban, bomb, anthrax) the activity
charts in the previous figure with the corresponding attraction charts in this
figure we see that they are “correlated” (obviously
act(a;t) = 0 implies att(a;t) = 0), but different
in details.
For example, the terms taliban and bomb have small attraction values at
the beginning of the time window – the terms were disguised by the
primary terms. On the other hand, the terms taliban and Kabul get
increased attraction towards the end of the time window.
In preparation. Not finished!!!
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