Science topics: MathematicsGraphs

Science topic

# Graphs - Science topic

Graph Theory is a branch of combinatorics, here we discuss the theory and application of graphs.

Questions related to Graphs

Distance Sequence!

A distance sequence of a tree is a non increasing one, consisting of the distances between any two leaves of the tree.
Given a distance sequence, can the tree be reconstructed?
Common I'm looking forward for your solutions! ;)

Does anyone know of good texts/books/examples discussing uncertainty in qualitative data visualizations (e.g. the graphical equivalent of significant figures or digits)? As a supervisor/reviewer I see many examples where students/authors present a graphical representation of their rather uncertain data, in the form of singular dots, straight sharp lines etc. There must be appropriate ways of visualizing this uncertainty. A reference to a guidebook or source book would be helpful.

This script will help to get the detailed pie diagrams, for GO analysis.

Our proxy setting is not allowing us to get all these graphs.

I am trying to analyze the dynamics of protein interaction networks using power graph analysis. I am unable to critically analyze the obtained graph, hence it will be very helpful if I get any inputs regarding analysis of power graphs.

Throughout literature I found that an Attack Graph is used for network security where it captures all the paths an attacker could use to reach their goals. Here I am looking for new real time application areas where I can use the concept of an attack graph. Any suggestion please.

Anybody working Seawater metal analysis using ICPMS with FIAS 400 (C-18 column) in this area send me journals paper including method and graph with data details.

Nowadays complex network data sets are reaching enormous sizes, so that their analysis challenges algorithms, software and hardware. One possible approach to this is to reduce the amount of data while preserving important information. I'm specifically interested in methods that, given a complex network as input, filter out a significant fraction of the edges while preserving structural properties such as degree distribution, components, clustering coefficients, community structure and centrality. Another term used in this context is "backbone", a subset of important edges that represent the network structure.

There are methods to sparsify/filter/sample edges and preserve a specific property. But are there any methods that aim to preserve a large set of diverse properties?

There is dagR package fro graph but commands are not very clear and i couldn't get any good commands for G-computation also, if anybody have, it would be appreciated.

I read many papers exploiting modularity maximization to divide networks in clusters.

Nowhere do I find a clear explanation of which value of modularity can be accepted as significant in order to conclude that a particular division of a network is well defined. Can anyone give some hints?

I am simulating an OTDR in Optisystem and I am using matlab to import the rayleigh scattering formula to Optisystem. When I run the Optisystem, it load the matlab code and the graph is plotted, but there is no output from matlab component on Optisytem though when I go to matlab and display the output it gives a result.

I have calculated the property (X) for 560 molecules. For some molecules, the values of the X are 0, for some1, for some2 like that and extends up to 10. Now, I want to draw A bar graph with range of the property (0-1, 1-2, 2-3, etc.,) on X-axis and the population (5%, 10%, ...) percentage on Y-axis (as shown in the graph present in the attachments) to find out the range of the property that is present in the maximum % of the population.

I have real data of various cellular components, and dispersion over time in various stress situations. I want to make an average 3d model per time in each stress using the data. I tried Virtual Cell and other softwares like that, but all are based on simulations and not on real data.

In diffuse phase transition the value of Gamma approaches to 2. But in my case I got the value of gamma greater than 2. What can be reason behind it?

I want to know the work done on connectivity of regular and triangle free graphs.

The workshop is part of the 2014 IEEE International Conference on Big Data to be held in Washington DC on October 27, 2014.

Program co-chairs: Jiangzhuo Chen, Sandeep Gupta.

Call for papers: Papers for the workshop ‘Big Data in Computational Epidemiology’ at IEEE BigData 2014 should be submitted through the IEEE BigData submission system (http://bit.ly/1oSAkAk). Please refer to IEEE manuscript templates (http://www.ieee.org/conferences_events/conferences/publishing/templates.html) for submission guidelines.

Important dates:

Aug 30, 2014: Due date for full workshop papers submission

Sept 15, 2014: Notification of paper acceptance to authors

Sept 25, 2014: Camera-ready of accepted papers

October 27-30, 2014: Conference

BCDE 2014 -- IEEE Workshop – Big Data in Computational Epidemiology

Computational epidemiology aims to understand the spread of diseases and efficient strategies to mitigate their outbreak. It studies dynamics in socio-technical systems, where disease spread co-evolves with public health interventions as well as individual behavior. It has evolved from ODE models to networked models which apply agent based modeling and simulation methodologies. Computation of such high resolution models involves processing data sets that are massive, disparate, heterogeneous, evolving (at an ever increasing rate), and potentially unstructured and of various quality.

The workshop brings together researchers from epidemiology, data science, computational science, and health IT domains to tap the potential of emerging technologies in data intensive computations and analytical processing to advance the state of art in computational epidemiology. The central theme of how to manage, integrate, analyze, and visualize vast array of datasets has wider applications in the bio- and physical- simulation and informatics based sciences such as immunology, high energy physics, and, medical informatics.

The workshop welcomes original research related to computational models and methodologies developed for handling big data and their application to epidemiology. Topics of interest include:

Collection and generation of large scale epidemiological datasets

Management, provenance, storage, and archival of surveillance, synthetic, and experiment data

Analytics of spatial, temporal, relational, and semi-structured data

Mining social media data and other online data for public health

Simulation driven statistical methods for knowledge discovery and forecasting

Cloud, streaming, and high performance data intensive science

Semantic web tools, informatics, inference, and integration of public health data

Privacy in the big data era

Agent mining, multi-agent systems, agent based modeling, and behavior modeling in epidemiology

Apart from representing the shortest path length distance among the vertices of the graphs and revealing the connectivity among its vertices, I would like to know if there is any other property that one can obtained from just observing the distance matrix of a given graph.

Graph theory and mathematical logic, are both parts of Discrete Mathematics syllabus. Some logical equalities can be express by rooted trees.

An updated sample selection is being determined for one of our most comprehensive energy establishment surveys, to more efficient estimate for the current population, as the sample has not been reselected in seven years. There are many combinations of categories for which we report, using small area estimation, and we would like to use some clear graphics to show management comparisons between test results using the past sample and the new one, also including before and after sample sizes, and before and after relative standard errors. I have JMP software on my PC at work, but I primarily just use it for running scatterplots. Does anyone have any suggestions for graphical presentations using JMP that would display the above comparative information well? Thanks.

I think both are time and space consuming, there must be something better!

I have got a model with four indicator variables and four interaction effects. Two of the interaction effects are relevant and significant at least on a 10% alpha level (N = 100).

Do you know a tool or a way to plot these interaction effects in one graph?

I have measured gene expression across eight time points, and multiple comparisons show many differences between the time points. I'd like to indicate these significant differences on my bar graph. Usually I would draw a line above the relevant bars and put * or ** above the line, but there are so many differences that the lines would become unwieldy and I feel my graph would just look too messy! Any suggestions? Should I just relegate the p values to a table?

Is blue vs. red and pink dead?

My earlier expectation regarding the sufficiency of 4CT to adress this issue, is not correct.

I have got first peak at 100 C which is the transition temperature from Ferroelectric to paraelectric according to literature. But at higher temperature i got another peak of dielectric maxima. What we can say about that peak. I here attach a graph.

Need to have access to cells that participate in Selective Boolean. Which kernels (opensource, or free-for-students) give access to the cell graph, inspite of being Brep/feature-based?

**How to fix error bar in Origin 6.0 graph? also how to fix error bar in tabular column?**

I need to determine all odd cycles in random cubic graph. I guess that complexity of this problem for general graph is exponential. Is the situation better for cubic graphs?

I understand the standard deviation, and how to get to standard errors, but not sure I understand the logic why standard errors could be used.

how can we present the statistics clearly ?

Seems this should be a FAQ, but I do not have the technical vocabulary for an effective search. I have a large number of observational units (~4000) in ten groups.

Let G=(X U Y,E,P) a weighted bipartite graph, we need to find a matching of maximum cardinality for which the length of the longest edge is minimized.

VosViwer is a very poferfull tool in bibliometric analysis but When I select the text corpus option, and then Scopus files I get either a run-time error message or a very sparse density graph, with just few terms analyzed.

If yes , Can anyone please answer with examples. Thanks

In a k-connected graph an edge is contractible if contraction of the edge yields a k-connected graph.

Currently I am measuring the binding of FITC labelled peptide to a protein using fluorescence polarization technique. However, even in the absence of peptide (buffer alone) I am getting higher fluorescence polarization value. Moreover, when I measured the fluoresce polarization of my FITC labelled peptide alone at various concentration from 100nM to 10uM, I am getting decreased fluorescence polarization upon increasing concentration of peptide. I wonder why buffer alone (131mP) or peptide at low concentrations (133mP) giving higher fluorescence polarization compared to peptide at higher concentration (10uM-31mP)

I am using Perkin Elmer 96-well plate reader (serial number-431460). Excitation wave length 485nm and emission 530nm and pH of buffer is 8.

I also attached the graph of FITC peptide fluorescence polarization at various concentrations.

Can anyone explain why buffer alone or peptide at low concentrations gives higher polarization?

Thank you.

I want to fix error bar in graph and tabular column in my paper. Please give the details how to fix it.

I have multiple excel files (~100) in a folder. Each time I have to process it to plot graph in matlab. Is there any coding to process all graph and plot it?

Is the graph that you found connected

I want to know the basic details of error bar in graph and tables.

I've been trying to compare my results to another study and I need these peaks d-spacing values in order to determine the average d-spacing to make the comparison but I'm not sure how to identify them. If anyone could give me any tips I would be most grateful, thank you!

I have problem with Rössler attractor. I do not know how to create bifurcation diagrams. I have tried to solve differencial equations by Runge Kutta 4, but my bifurcation diagrams are still wrong despite that graph of Rössler attractor is correct. Please, could you advice me how to establish bif. diagram correctly. I do something wrong but I do not know what.

I have a graph generated from Globplot which I cannot understand. The only thing I can understand is that there is a continuous stretch of disordered region from approximately residue number 800.

I'm obtaining poor values in higher paper. On the same conditions with a BCR of 160 I'm obtaining CMT=350N and with a BCR=190 CMT=190N. And the graphs are completely different. I am very confused.

Thank you very much.

I got the scheme graph as attached. Can I determine the molecular weight of the polymer? I don't understand whether the data is correct or not.

Dear all,

I would like to plot radial density curve for my spherical lipid system. I have plotted RDF graph using gromacs utility g_rdf as shown below:

gmx rdf -f traj.xtc -s topol.tpr -n index.ndx -o rdf.xvg -cn rdf_cn.xvg -bin 0.05 -com

How to post-process the output (rdf.xvg or rdf_cn.xvg) to get radial density plot (1/nm^3 vs nm)?

Thank you.

How to display results in graph, plot or hyperplane of svm with w ,b and svs etc.

I am looking for a compact encoding of directed graphs, in particular regarding the linear (without pointers) encoding, suitable for streaming.

I know the vertex set of total graph T(G) is the union of V(G) and E (G). Two vertices x,y in the vertex set of T(G) are adjacent in T(G) if any one of the following holds

(i). x,y are in V(G) and x is adjacent to y in G

(ii). x,y are in E(G) and x,y are incident in G

Solution needed within 2 days

Given an undirected graph G=(V,E) where V=V1 U V2 U V3 and
E is a subset of { {u,v} | u is in V2 and v is in (V1UV3) }
Now the problem is to determine a set X which is a subset of V2 such that
TP+TN become maximum where:
TP= | {v : v is in V1 and there exists a vertex u in X such that uv is in E } |
TN= | {v : v is in V3 and there is no vertex u in X such that uv is in E } | .

What are the sufficient conditions to be satisfied by a graph for, it is not Hamiltonian?

Is there any software to various types on named graphs and to study about their properties?

The concept of semi graph is new. So I am working on this topic.

On the CPAN, we find Graph 0.94 (by Hietaniemi), but it only gives betweenness centrality (and false results for diameter). There is also BoostGraph (only shortest paths).

Does anyone know Perl modules to compile clustering coefficient and any modularity or graph segmentation algorithm ? How would it be possible to use within Perl the Gephi modules (written and freely available in Java) ? Is there something like Jython in Perl ?

In frequency assignment, it is very important to find a lower bound of minimum span of frequency assignment problem. Up to 2007 the lower bound are known but after that I don't know whether this bound was improved or not?

In its 0.8.1, the Gephi software provides a community detection algorithm with a tunable "resolution". It is a great tool that enables changing the focus of the detection of communities. Give a smaller or bigger resolution, and you have more or less classes.

But there is no bibliographic reference to explain where this resolution comes from. Unless I have not noticed it, the Blondel et al. (2008) paper does not provide any resolution tuning. Does anyone have have first-hand sources about this tuning ?

It can be advocated that a graph is a powerful representation for several kinds of systems. Does anyone use them to study self-organizations? It think it has been argued that the study of the degree power law of a graph could give clues for the evolution of the system.

Any further idea?

I use the "Louvain" algorithm (Blondel et al., 2008) in Gephi to detect communities in graphs. This algorithm provides a modularity value Q as sort a "quality-control". The higher the value is, the best the result should be.

Gephi (versions < 0.7) said that it Q < 0.4, the result should not be considered too seriously. Is this an absolute answer? Are partitions with Q < 0.4 really bad results? Uninterpretable results? Nonsense results?

Watts, Strogatz (1998) tell that their clustering coefficient denotes sort of a "cliquishness". Does it refer to homogeneity of a subpart of a graph? I'm working on lexical text graphs (a node represents a word, an edge represents co-occurrence of two words) and would be interessed to learn interpretations of this metric.

Ulam's conjecture:

Let graph G have p points v_i and graph H have p points u_i, where p>=3. Then if for each i, the subgraphs G_i=G-v_i and H_i=H-u_i are isomorphic, then the graphs G and H are isomorphic.

What are the current topics of research interest in the field of Graph Theory?

Open Research Topic • Logical Graphs

I recently found out that there is a link between planar graphs and distributive lattices, e.g. certain sets of orientations of a planar graph form distributive lattices. When i read a theorem stating that a lattice is distributive if and only if it has no sublattice isomorphic to N5 (the pentagon lattice) and M3 (the diamond lattice) it struck me that there is a huge apparent similarity to the planarity argument in graphs where a graph is planar (on S0) if and only if it doesn't not contain a subdivision of the graphs K5 (the complete graph with 5 vertices) and B3,3 (the complete bipartite graph with 2 partitions with 3 vertices each).

Since my actual work is in a different field i cannot spare the time to really investigate this on my own, yet i am very interested in a possible connection here. So does anyone here know of a paper/thesis or similar that expounds on or disproves this apparent similarity?

Give me detail about supersubdivision of graphs

Given an arbitrary graph, Is there any method to construct a bipartite graph which represents the given graph faithfully.

hey all wonderful people here,

can we discuss different efficient algorithms for matching of two graphs....

warm regards,

Chetan

Can we find algorithm used in facebook friends location Graph? It wonderfully creates a graph according to friends locations

Vertex coloring in which no two neighbors of the same vertex are having same color

List colorings was defined by Vizing/1976, Erdos et al./1979.

I thought bar graphs with different colors for each group, but Excel nor STATA include this option.

I'm implementing the algorithm in the context of grammatical dependency graph pattern matching. The "simple enumeration algorithm" is fine but I'm not familiar enough with matrices to understand why condition (1) works. What is M(MB)^T = C, semantically?

Thanks

Does anybody have a good book or articles, an introductory one, on the Pythagorean graphs?

When plugging in errors for a simple bar chart of mean values, what are the statistical rules for which error to report? I guess the correct statistical test will render this irrelevant, but it would still be good to know what to present in graphs.

Can any body help me in finding out what are the current trends in Random Graphs?

I want to read and work in Random Graphs.

We define state space and map input in state space and output as a combination of the state variable. If we define a transfer function with two variables s1 and s2, then how can we define the minimal state space form for this transfer function?

For example, if T(s1,s2)=(s1+s2)/s1s2-1, and the minimal state space has an imaginary value in minimal state space, what is the meaning of imaginary in this realization? And how can we migrate from complex variables to real, and import a real state space from this realization?

The only pertinent reason I am able to identify is for presentation purposes. That is, sometimes data points cluster at the really low end of the concentration axis, making it very difficult to see what is happening. But where that is not the case, is there any other reason that compels me to log my concentrations?

I want to delete a given number M of vertexes (with adjacent edges) of a given graph G with N vertexes to split the remaining graph G' into minimal number of independent sets. Or, alternatively, to color it with a min number of colors. Graphs are sparse and huge, M is much less then N.

I am working on circular layout, I need a mechanism to pass vertices of a graph in some particular order (for edge crossovers minimization) to circle graph layout. But there is no parameter in circle graph layout definition which accepts the vertex ordering.

Is there any way I could do that? Or how shall I change the vertex sequence of a boost graph so that circle graph layout will automatically plot the vertices in desired sequence ?

I am working on clustered graph using boost subgraph.

I have tried to search this topic over internet, however, I can't get a very clear picture about bi-partitioning. Understanding that bi-partitioning is splitting a graph into 2 partition, my concern is that the do the partition have to be symmetric in term of size, weight, and so on?

Could someone assist me on how to display a graph in python?

I have downloaded and installed the canopy enthought environment for my python code.

However, when I use the plot command from pylab, it does not display the graph even though no error is generated. I am quite new to python. Kindly tell me how to display the graph.

Below is the sample code:

import numpy

import pylab

pylab.interactive(True)

y=numpy.array([2,3,4,5,6,6])

print y

x=pylab.linspace(-1, 1, 20)

pylab.plot(x, x**2, 'o-')

For a given integer n, how to determine the smallest size of graphs satisfying the Ore-condition? i.e., finding the minimum graphs that satisfy the sum of the degrees of any non-adjacent vertices being at least n.

Grappa Libraray

How to setup with grappa java libarary?
http://www2.research.att.com/~john/Grappa/

What is the application of irregular labelling or total vertex irregular labelling?

My project is about total vertex irregularity strength, and some of my friends ask about it's application.

I face the task of laying out a weighted graph that will change the number of nodes during runtime. The chosen layout should rearrange the nodes without losing the global topology in order to keep the "amount of visual adjustment" as low as possible for the user. Additional requirement: it should be applicable in a JAVA-environment. Does anyone know the best practices?

How can we draw median graphs with a diameter of 8?

Is there any way to draw median graphs of diameter 8?

Is there a well defined structure to keep graphs in memory?

I'm looking for all the techniques used to prove hamiltonicity

I am creating a project which "transforms the C code" to flow graph. Please suggest any tool or any materials. Should I change the compiler intermediate code to any specification of graph transformation system?

Csv file consists of x, y, z (three colums). I have to load those three colums values into matrix & then i have to plot x vs y, x vs z.

x y z

1 11 13

2 8 45

3 13 18

4 85 106

Complex networks (CN) are one of the most important type of graphs in recent years. Publications of Watts-Strogatz (Small World property, 1998) and Barabasi-Albert (Scale Free property, 1999) have started intensive research in this area. CN have been the most often considered in static way. What's about dynamics (evolution) of CN?

Tell me about the generation of uml diagram to graph model.

Conversion of Class diagram to graph model