understanding network structure through user attributes and behavior

Understanding Network Structure Through User Attributes and Behavior

Describe this network

What does it mean?

Hints Built from the photo sharing website Flickr. On Flickr, photos are labeled with descriptive keywords

called tags. Nodes represent tags and an edge between tags indicate

that they were used to describe the same image. E.g. if an image is tagged with the word “desk” and

“keyboard” the network would show a line connecting those two words.

Network is a 1.5 egocentric network of a single tag

What can we say now?

Now with content...

Connecting Content and Structure

Structural attributes only tell us a little

Must look at data about nodes and edges to really understand what is happening in a network

Node X has high betweenness is only a description of a statistic

Node X has high betweenness, and the data shows he connects a group of people from the US with a group of people from Spain tells what his role is and why it is important.

Example Analysis

Example Analysis

Network is 1.5 egocentric network of a search term on YouTube

Nodes represent videos that match the search term

Links indicate videos share at least one other keyword in common

More Data

Search term is “cubs”

Initial thoughts about what you see in the network?

Getting into content: Graph Level

Choose a few videos from each cluster and watch them See what they are about Look at their keywords

Selected nodes in white and black

White Nodes’ Keywords

Cubs, CubFans, baseball, Chicago, Please, Stop, Believing

mlb, 2k12, baseball, major, legaue, ronnie, woo, wilckers, wrigley, cubbies, north, side, billy, goat, curse, illinois, ps3, playstiation, cubs

MLB, 12, The Show, MLB 2k12, Diamond Dynasty, Baseball, triple play, world series, home run derby, PS MOVE, Jose Bautista Chicago, Cubs, win, sports, playstation, ps3, ps vita, video game, so real it's it’s unreal

Chicago Cubs, Chicago, Cubs, Wrigley Field, Opening Day, 2011, number one fan, sports fans, baseball, major leagues

Chicago, Cubs, Spring, Training, Baseball, Tony, Campana, Brett, Jackson, Sports, Hohokam, Park, Cactus, League

Black Nodes’ Keywords

dog, dogs, puppies, pup, cute, adorable, snuggle, bear cub, Medvjedić, Bär, orsacchiotto, brown bear cub, bears, teddy, medo srečko, cubs, medvedji mladič, slovenia, slovenija

National Geographic, polar, bear, cubs, mother, mom, parent, learn, teach, cute, fluffy, sweet, predator, arctic, predation, hunt

Tiger, Rescue, Lions, Leopards, Cubs, Kittens, Tiger cubs, Wild animal orphanage, Big Cat Rescue, Texas, Tigers, Rescued, Scary, Roar, Rawr, Attack, Aggressive, Sanctuary, Global

tiger, tigress, cubs, machli, fight, nick, ranthamore, croc, crocodile, mugger, india, rajastan, valmik, thapar, bbc, wildlife

cheetah, cheetahs, african, wild, cute, animals, baby, BBC, cubs

Conclusions

The cluster of nodes with the white samples represent videos about the baseball team the Chicago Cubs

The cluster of nodes with the black samples represent videos about baby animals (bear cubs, tiger cubs, etc.)

Getting into Content: Node Level

Individual nodes may represent different types in a network

This requires understanding node attributes and linking it to the role in the network.

Example: Nodes colored by department

Example: Detecting User Roles

Study by Welser, Gleave, and Smith, 2007.

Examined the roles users play in discussion groups

Example Network

Breaking Down Into Egocentric Nets

Observations

36 nodes have only one neighbor, and in almost all cases that neighbor has a high degree and had replied to the central node.

Another 17 nodes have two neighbors with this same pattern.

This accounts for nearly 60% of the nodes in the network.

Do these nodes have something in common?

Diving Into Content

Group nodes by attributes of their egocentric networks

Look at the behavior of those nodes in discussion groups to see if there are patterns

This involves actually reading their posts and understanding the communication on a content level, not just a network structure level.

Findings

Nodes with high out degree and low network density tend to answer a lot of questions, but not engage in a lot of discussion

Nodes with low degrees are generally asking questions. They get a reply and then stop participating.

Many other patterns found by researchers

These results rely on connecting structure to content

Conclusions

To understand a network, we need more than structural attributes

Connecting structure with analysis of content can lead to much deeper insights about what is happening in a network.

This is a connection is critical for full, deep, and insightful network analysis.