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Asking for help in editing my dissertation's abstract - Nilly
August 15th, 2008
01:45 pm

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Asking for help in editing my dissertation's abstract
Re-posting from b.org:

Would any of you clever with-actual-knowledge nice people like to be brutally take-no-prisoners-ly nitpicker-ly honest in tearing apart my abstract so that I can improve it? Any sort of remark is more than welcome - obviously, the language and grammar, as well as sentence and paragraph structure, but of course I'd love any comment on pretty much anything and everything you find is lacking.

It's still somewhat at a draft stage, considering I've written and re-written and deleted and tweaked and went back again and lather-rinse-repeat so much, I can't see anymore what works and what doesn't. I know I have some crazy long and convoluted sentences in there, and that I have to cut it a bit, but I am too close - technically - to all this mess right now to be able to properly edit. So I turn to you guys.

Instead of bogging Natter down with too much meMeME, I posted it here. Comments there, here, e-mails to my profile address, smoke signals or little paper airplanes are all not only welcome, but deeply appreciated.

(I will only see them after shabbat, though, so my actual corrections and questions for clarification and returning small paper airplanes will be delayed. It's not that I'm not filled with gratitude, it's just that I'm sleeping.)

Here goes:
Dynamics of Data Traffic in Communication Networks

Networks play an important role in many research areas, including physics, biology, sociology, computer science, and electrical engineering. Communication networks, most notably the Internet, have become a critical resource of modern life. Not only does the amount of data traffic in them keep growing, but its various types and uses keep expanding, as well.

Classical models for networks are based on regular lattices and random graph theory derived by Erdos and Renyi in the 1960's. In recent years it became clear that these models were insufficient as a description to many real world networks, which exhibit different characteristics. The most notable difference is in the degree (number of links per node) distribution, which, in real world networks is usually a power-law, in contrast to the Poisson distribution predicted by random graph theory. Such structures have fractal properties, i.e., have no characteristic scale, and are therefore called scale-free networks.

The structure of these networks was thoroughly studied, both in theoretical models as well as in real world networks. On top of the   comprehensive understanding of the topology of such networks, many properties deriving from this topology were explored, as well. For example, their very small mean hop distance, resilience to random attacks and vulnerability to targeted ones.

A question related to this abovementioned network's stability is the dynamics of virus and worm spreading through it. A network which is not resilient to random breakdown of nodes is also vulnerable to such infections. Efficient immunization of large computer networks becomes extremely important in light of epidemic spreading in human populations as well as large productivity losses due to virus infections.

We explored several efficient immunization strategies, i.e. strategies for selecting key nodes for immunization and blocking of viruses and worms. Immunizing random nodes had the benefit of not requiring any previous knowledge of the system, but a large percentage of immunized nodes was needed in order to stop the epidemic. Immunizing the most highly connected nodes enabled a very small fraction of immunized nodes to stop the spread of the epidemic, however it also required full knowledge of the entire system.

An immunization strategy which combined both advantages of the two abovementioned strategies was explored as well - the acquaintance immunization strategy. Immunizing random neighbors of randomly chosen nodes required no prior knowledge of any of the system's nodes. However, due to the scale-free nature of the network, there was a high probability that the randomly chosen immunized neighbor was one of the highly-connected nodes (hubs) of the system, therefore ensuring that a relatively small fraction of immunized nodes managed to block the spread of the epidemic. A double-acquaintance immunization strategy, repeating the random choice process, was studied as well.

But the static characteristics of epidemics and immunizations are not enough for a complete picture, since they cannot capture the time-dependent progress of the disease, the time required for extinguishing it, the fraction of infected nodes at each time step and so forth.

Therefore, we also studied these dynamic properties, both regarding the spreading of epidemics as well as in assessing the efficiency of the various immunization strategies against them. We used the well-known SIR model, describing diseases dynamics, as well as its mapping to known results from percolation theory.

Naturally, this is not the single area in which the dynamics of the flow of the data through the network is affected by the network's topology. The deep and comprehensive description of that topology as well as its effect on the netwrok's properties makes it possible to take the next step and use it not just for the static characteristics of the network, but for attempting to describe and even optimize the dynamic properties, as well.

Many communication networks are highly dynamic by nature. Nodes and links may constantly be added or removed and links become congested. Thus, to ensure reliable and efficient operations on the network, algorithms for dynamical routing of the data flowing through it are required, either optimal or sub-optimal. The current existing algorithms, employing large tables of data, whose pace of update isn't quick enough, do not give a good enough answer to the problem.

The size and complexity of such communication networks may be dealt with through an efficient routing or search algorithm employing heuristics and taking the scale-free network structure into account. We investigated extensively the first steps for formulating such an efficient dynamic routing algorithm, based on the A* algorithm, which is a well-known heuristic search that is used to find an optimal path in a graph.

Search algorithms are a well-studied field in computer science, with algorithms ranging from Brute-Force simplistic ones to sophisticated heuristics. Despite that, no attempt to use the underlining topological structure of the network was conducted so far, in order to improve the running time of a search algorithm.

We suggest a heuristic search, based on the well-known A* search algorithm, which takes into account the actual topology of the network. The logic behind it follows similar lines to those behind the acquaintance immunization strategy mentioned above, namely that there is a high probability that a random neighbor of a random node is one of the highly-connected nodes.

Our A* search assumes that each node contains a small information table, regarding a fixed number of nodes. Once each node is expanded, not just his nearest neighbors are generated, but also the nodes in its information table. Thus, the search for the shortest path between two random nodes, can enjoy the high probability of such a path going through a hub, and of that hub to be included in the abovementioned information table, and its running time shortens accordingly.

Extensive statistical analysis was conducted regarding the various characteristics of the A* search and its improved efficiency over simple searches (mainly the Breadth-First-Search, also known as BFS). Several variations of it were taken into account, with different information table sizes, either of a fixed number of nodes or of a certain percentage of the whole network, and more. We showed that a dramatic improvement in the search's efficiency was achieved even for very small information-table sizes, regardless of the size of the network, and even the possibilities of its future growth.

Finally, in order to study network performance, topology is not the only factor, but one needs also to consider traffic. Thus, we need to better understand the traffic's spatial and temporal behavior. In the study of vehicular traffic, similar analyses were a crucial step in developing optimization strategies for better traffic flow.

Various measurements have shown that data packet interarrival times are correlated over a long term and much more heterogeneous than the classical Poisson model. We applied statistical physics methods such as detrended fluctuation analysis (DFA) in order to deepen our understanding of the data traffic, and help in combining the our findings into developing better data routing algorithms for traffic flow on scale-free networks.

(14 comments | Leave a comment)

Comments
 
From:mskat
Date:August 15th, 2008 04:18 pm (UTC)

I'm just tightening it a little and reducing passive voice. My changes in italics.

(Link)
... Not only does the amount of data traffic in them keep growing, but its various types and uses keep expanding, as well.While the amount of data traffic expands, so do the variety of data types and uses.

Classical models for networks are based on regular lattices and random graph theory derived by Erdos and Renyi in the 1960's. In recent years it became clear that these models werethese models appeared insufficient as a descriptions to many real world networks, which that exhibit different characteristics. The most notable difference is in the degree (number of links per node) distribution, which, in real world networks is usually a power-law, in contrast to the Poisson distribution predicted by random graph theory. Such structures have fractal properties, i.e., have no characteristic scale, and are therefore called scale-free networks.

The structure of these networks was thoroughly studied, both in theoretical models as well as in real world networks. Researchers thoroughly studied the structure of these networks, both in theoretical models as well as in real world networks. On top of the comprehensive understanding of the topology of such networks, many properties, such as their very small mean hop distance, resilience to random attacks and vulnerability to targeted ones, were explored as well. as deriving from this topology were explored, as well. For example, their very small mean hop distance, resilience to random attacks and vulnerability to targeted ones.

A question related to this abovementioned network's stability is the dynamics of virus and worm spreading through it. A network which is not resilient to random breakdown of nodes is also vulnerable to such infections.Efficient immunization of large computer networks becomes extremely important in light of epidemic spreading in human populations as well as large productivity losses due to virus infections. Since a network which is not resilient to random breakdown of nodes is also vulnerable to viruses and worms, efficient immunization of large computers networks becomes extremely important, particularly because such infections cause large productivity losses and are susceptible to epidemic spreading in human populations. (though maybe you don't want a compound-complex sentence as it's own paragraph?)

....

An immunization strategy which combined both advantages of the two abovementioned aforementioned strategies was explored as well - the acquaintance immunization strategy. .....

But the static characteristics of epidemics and immunizations are not enough for a complete picture, since they cannot capture the time-dependent progress of the disease, the time required for extinguishing it, or the fraction of infected nodes at each time step and so forth.
.....

Naturally, this is not the single area in which the dynamics of the flow of the data through the network is affected by the network's topology. Naturally, the network's topology affected other aspects of the dynamics of the flow of the data through the network.The deep and comprehensive description of that topology as well as its effect on the netwrok'snetwork's properties makes it possible to take the next step and use it not just for the static characteristics of the network, but for attempting to describe and even optimize the dynamic properties, as well.

Many communication networks are highly dynamic by nature. Nodes and links may constantly be added or removed and links become congested. Thus, to ensure reliable and efficient operations on the network, either optimal or sub-optimal algorithms for dynamical routing of the data flowing through it are required, either optimal or sub-optimal. The current existing algorithms, employing large tables of data, whose pace of update isn't quick enough,with slow updates do not give a good enough answer to the problem.solve the problem.


Edited at 2008-08-15 04:19 pm (UTC)
[User Picture]
From:spectralbovine
Date:August 15th, 2008 06:05 pm (UTC)

Re: I'm just tightening it a little and reducing passive voice. My changes in italics.

(Link)
Okay, you did a better job than I did. Heh. I need to get more comfortable with messing with other people's words on more than a superficial level.
From:mskat
Date:August 15th, 2008 06:14 pm (UTC)

Re: I'm just tightening it a little and reducing passive voice. My changes in italics.

(Link)
I tried to keep everything intact since I know almost zero about the content. I have to take a pass at the second half, which is more content heavy.
From:mskat
Date:August 15th, 2008 09:04 pm (UTC)

Re: I'm just tightening it a little and reducing passive voice. My changes in italics.

(Link)
The size and complexity of such communication networks may be dealt with handled through an efficient routing or search algorithm employing heuristics and taking the scale-free network structure into account. We investigated extensively the first steps for formulating such an efficient dynamic routing algorithm, based on the A* algorithm, which is a well-known heuristic search that is used to find an optimal path in a graph.

Search algorithms are a well-studied field in computer science, with algorithms ranging from Brute-Force simplistic ones to sophisticated heuristics. Despite that, no attempt to use the underlining topological structure of the network was conducted so far, in order to improve the running time of a search algorithm. Yet, no study has examined using the underlining topological structure of the network with an attempt to improve the running time of a search algorithm.

We suggest a heuristic search, based on the well-known A* search algorithm , whichthat takes into account the actual topology of the network. The logic behind itthis algorithm follows similar lines to those behind the acquaintance immunization strategy mentioned above, namely that there is a high probability that a random neighbor of a random node is one of the highly-connected nodes.

Our A* search assumes that each node contains a small information table, regarding with a fixed number of nodes. Once each node is expanded , not just his nearest neighbors are generated, but also the nodes in its information table.both the nodes of its nearest neighbors and the nodes in the information table expand Thus, the search for the shortest path between two random nodes , can enjoy the high probability of such a path going through a hub, and of that hub to be included in the abovementioned information table, and its running time shortens accordingly.



Edited at 2008-08-15 09:06 pm (UTC)
From:mskat
Date:August 15th, 2008 09:10 pm (UTC)

Last bit

(Link)
Extensive statistical analysis was conducted We conducted extensive statistical analysis regarding the various characteristics of the A* search and its improved efficiency over simple searches (mainly the Breadth-First-Search, also known as BFS). Several variations of it were taken into account, with different information table sizes, either of a fixed number of nodes or of a certain percentage of the whole network, and more. We showed that a dramatic improvement in the search's efficiency was achieved even for very small information-table sizes, regardless of the size of the network, and even the possibilities of its future growth.I'm not sure what you mean here. do you mean that the improvement happened even with the possibility of its growth?

Finally, in order to study network performance, topology is not the only factor, but one needs alsoalso needs to consider traffic. Thus, we need to better understand the traffic's spatial and temporal behavior. In the study of vehicular traffic, similar analyses were a crucial step in developing optimization strategies for better traffic flow. and therefore, we need to also ....?

Various measurements have shown that data packet interarrival times are correlated over a long term and much more heterogeneous than the classical Poisson model. We applied statistical physics methods such as detrended fluctuation analysis (DFA) in order to deepen our understanding of the data traffic, and help in combining the our findings into developing better data routing algorithms for traffic flow on scale-free networks. I would think this paragraph should go before the one that starts with "Finally"

I hope any of this helps!If not, feel free to disregard! You are in my thoughts.

[User Picture]
From:nilly_madar
Date:August 16th, 2008 06:16 pm (UTC)

Re: Last bit

(Link)
Kat, you're amazing! Thank you so much. I really appreciate this.
[User Picture]
From:spectralbovine
Date:August 15th, 2008 06:02 pm (UTC)
(Link)
Not only does the amount of data traffic in them keep growing, but its various types and uses keep expanding, as well.
You've been talking in plurals the whole time until now. I think you mean this to be "their," if it's still referring to "networks." If you mean "the Internet," you should clarify that and also not switch number in the middle of a sentence.

Classical models for networks are based on regular lattices and random graph theory derived by Erdos and Renyi in the 1960's.
No apostrophe! 1960s.

The most notable difference is in the degree (number of links per node) distribution
I would move the parenthetical to after the noun so you don't break the flow in the middle of the phrase (...the degree distribution (number of links per node).

which, in real world networks is usually a power-law,
Rogue comma after "which." I have no idea what a power-law is.

A question related to this abovementioned network's stability is the dynamics of virus and worm spreading through it.
Spreading through what? The antecedent right now is "stability," when I believe you mean "network." So you should just say "spreading through the network."

A network which is not resilient
which -> that

Efficient immunization of large computer networks becomes extremely important in light of epidemic spreading in human populations as well as large productivity losses due to virus infections.
This sentence loses me at the end because I can't tell what the "as well as" is referring to. Are the losses becoming extremely important or are they part of the "in light of"? I think you might just want an "and" there instead.

Immunizing the most highly connected nodes enabled a very small fraction of immunized nodes to stop the spread of the epidemic, however it also required full knowledge of the entire system.
Semicolon after "epidemic," comma after "however."

An immunization strategy which combined both advantages
which -> that

But the static characteristics of epidemics and immunizations are not enough for a complete picture
HOLY STARTING A SENTENCE WITH A CONJUNCTION, BATMAN!

since they cannot capture the time-dependent progress of the disease, the time required for extinguishing it, the fraction of infected nodes at each time step and so forth.
I don't like this "so forth" business. It seems too informal, like you're just shooting from the hip rather than writing an academic paper.

We used the well-known SIR model, describing diseases dynamics, as well as its mapping to known results from percolation theory.
Should that be disease dynamics? Or diseases' dynamics? Also, you have antecedent issues again. Whose mapping? The model's or the dynamics'? I think you might want a dependent clause for the disease dynamics to avoid confusion (...model, which describes disease dynamics, as well as its mapping to...). Actually, that's still a little confusing because now I think the "as well as" may be connected to the describing rather than the using.

The deep and comprehensive description of that topology as well as its effect on the netwrok's properties
La la la.

The deep and comprehensive description of that topology as well as its effect on the netwrok's properties makes it possible to take the next step and use it not just for the static characteristics of the network, but for attempting to describe and even optimize the dynamic properties, as well.
Nilly, I think you have an As Well problem. We need to check you in to As Well rehab.

Thus, the search for the shortest path between two random nodes, can enjoy
Rogue comma after nodes.

Finally, in order to study network performance, topology is not the only factor, but one needs also to consider traffic.
Aargh, flipping your subjects! These clauses should be parallel: "Topology is not the only factor: traffic must also be considered." Or, say, "one must consider traffic in addition to topology." Or something. Merge the two concepts together.
[User Picture]
From:spectralbovine
Date:August 15th, 2008 06:02 pm (UTC)
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Okay, that was a superficial look at your words and sentences. I think some of the things I pointed out are recurring issues (like confusing antecedents, awkward sentence structure, and an overuse of "as well") that you can probably see with new eyes now. My brain is now mush, however, because I eventually got totally lost in all the nodes and algorithms and technobabble. If I can, I'll take a second look and give more high-level comments.
[User Picture]
From:nilly_madar
Date:August 16th, 2008 06:15 pm (UTC)
(Link)
Thank you anyway, for all you posted above.

I have your corrections above, along with Kat's and Ginger's, and they're more than enough for the little time I have to go over them and insert them, anyway. But thank you even for the intent, just the same.
[User Picture]
From:gingerk
Date:August 15th, 2008 07:47 pm (UTC)
(Link)
Nilly, I sent you an edited version. I hope it helps.
[User Picture]
From:nilly_madar
Date:August 16th, 2008 06:14 pm (UTC)
(Link)
Thank you so much, ginger! You did so much work! I really appreciate it.
[User Picture]
From:gingerk
Date:August 16th, 2008 06:32 pm (UTC)
(Link)
Once I have copy in front of me, my brain goes into editor mode. I really only have one speed. Anyway, I hope you can use some of it.
[User Picture]
From:sowilo
Date:August 15th, 2008 08:08 pm (UTC)
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Possibly stupid question, but is the abstract going to be submitted in English?
[User Picture]
From:nilly_madar
Date:August 16th, 2008 06:13 pm (UTC)
(Link)
Not a stupid question at all!

There will be abstracts both in Hebrew and in English.

The whole dissertation will be submitted in English, as well (in a way, it's much easier to write in English, since all the material anyway exists only in English to begin with, and pretty much being thought about - and sometimes, even discussed - in English). It has to be an OK English, of course, but the most important thing to phrase as properly as possible is the abstract, since it passes many hands and is being read by many people (some people along its way to referees and such read only that).
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