Predicting Graph Categories from Structural Properties

Complex networks are often categorized according to the underlying phenomena that they represent such as molecular interactions, re-tweets, and brain activity. In this work, we investigate the problem of predicting the category (domain) of arbitrary networks. This includes complex networks from different domains as well as synthetically generated graphs from five different network models. A classification accuracy of 96.6% is achieved using a random forest classifier with both real and synthetic networks. This work makes two important findings. First, our results indicate that complex networks from various domains have distinct structural properties that allow us to predict with high accuracy the category of a new previously unseen network. Second, synthetic graphs are trivial to classify as the classification model can predict with near-certainty the network model used to generate it. Overall, the results demonstrate that networks drawn from different domains (and network models) are trivial to distinguish using only a handful of simple structural properties

Observing Light Variations and Period Analysis of Proto-Planetary Nebulae

Our research project was the observational study of a class of evolved stars called proto-planetary nebulae. These are stars in the transitional phase between red giants and white dwarfs. Most of our research was carried out at the VU Observatory (VUO), where we used the computer-controlled telescope with 16-inch mirror to observe 28 objects on a total of 14 nights (thus far) this summer. For this research, we use a charged-coupled device (CCD) camera to take images of these stars and their comparison stars in order to compare their brightness variations. Exposure times range from 1 second to 45 minutes, depending upon the brightnesses of the stars. These data, combined with VUO data from previous summers, will be analyzed using a period-finding program to determine the periods and amplitudes of these objects’ variations of light. This information is useful in order to gather more knowledge on the interior of evolved stars as well as their physical properties

Observing Light Variations and Period Analysis of Proto-Planetary Nebulae

Our research project was the observational study of a class of evolved stars called proto-planetary nebulae. These are stars in the transitional phase between red giants and white dwarfs. Most of our research was carried out at the VU Observatory (VUO), where we used the computer-controlled telescope with 16-inch mirror to observe 28 objects on a total of 14 nights (thus far) this summer. For this research, we use a charged-coupled device (CCD) camera to take images of these stars and their comparison stars in order to compare their brightness variations. Exposure times range from 1 second to 45 minutes, depending upon the brightnesses of the stars. These data, combined with VUO data from previous summers, will be analyzed using a period-finding program to determine the periods and amplitudes of these objects’ variations of light. This information is useful in order to gather more knowledge on the interior of evolved stars as well as their physical properties

Observing Light Variations and Period Analysis of Proto-Planetary Nebulae

Our research project was the observational study of a class of evolved stars called proto-planetary nebulae. These are stars in the transitional phase between red giants and white dwarfs. Most of our research was carried out at the VU Observatory (VUO), where we used the computer-controlled telescope with 16-inch mirror to observe 28 objects on a total of 14 nights (thus far) this summer. For this research, we use a charged-coupled device (CCD) camera to take images of these stars and their comparison stars in order to compare their brightness variations. Exposures times range from 1 second to 45 minutes, depending upon the brightnesses of the stars. These data, combined with VUO data from previous summers, will be analyzed using a period-finding program to determine the periods and amplitudes of these objects’ variations of light. This information is useful in order to gather more knowledge on the interior of evolved stars as well as their physical properties

Observing Light Variations and Period Analysis of Proto-Planetary Nebulae

Our research project was the observational study of a class of evolved stars called proto-planetary nebulae. These are stars in the transitional phase between red giants and white dwarfs. Most of our research was carried out at the VU Observatory (VUO), where we used the computer-controlled telescope with 16-inch mirror to observe 28 objects on a total of 14 nights (thus far) this summer. For this research, we use a charged-coupled device (CCD) camera to take images of these stars and their comparison stars in order to compare their brightness variations. Exposures times range from 1 second to 45 minutes, depending upon the brightnesses of the stars. These data, combined with VUO data from previous summers, will be analyzed using a period-finding program to determine the periods and amplitudes of these objects’ variations of light. This information is useful in order to gather more knowledge on the interior of evolved stars as well as their physical properties

Network Classification and Inferencing

Currently, there is no definitive method for classifying networks into distinct categories. The leading method in network classification involves using Support Vector Machines (SVM) to identify subgroups within a broader category, often a specific field of investigation. By looking at data mining classification methods, and feature analysis, this work seeks to classify networks into meta-categories with high accuracy. Preliminary investigations have been conducted with the Network Repository data from BHOSLIB, DIMACS, DIMACS10, Retweet Networks, Social Networks, and Temporal Reachability networks. SVM has revealed promising results with a classification accuracy of 67.5%. This was achieved by excluding Total Triangles. In addition, Naive Bayes has shown good results with the exclusion of the attribute maximum triangles. It has produced a classification accuracy of 84.3%. Identifying the best algorithm and the best features to consider will lead to a more procedural and efficient way of classifying graphs into these meta-categories. This will be useful to the wider scientific community by allowing them to more easily choose effective algorithms for graph mining and investigations