Tasks Taxonomy for Graphs
fioricet online order lipitor cheap ultracet cheap tenuate soma online cheap phentermine vicodin online ortho online vicodin online levitra online adipex free motorola ringtones order adipex nextel ringtones order ultram nokia ringtones free funny ringtones buy carisoprodol phentermine online valium online free qwest ringtones cheap tenuate sonyericsson ringtones motorola ringtones cyclobenzaprine online norco online cheap lortab cheap flexeril tracfone ringtones carisoprodol online paxil free qwest ringtones buy tramadol buy levitra didrex online punk ringtones ultracet online hydrocodone online buy xenical nokia ringtones free sprint ringtones buy diethylpropion adipex free polyphonic ringtones cialis online ultram online clomid online sagem ringtones nextel ringtones alprazolam online ultram online cheap alprazolam samsung ringtones cheap viagra motorola ringtones viagra online phentermine online cheap ultram diazepam online rivotril online cheap ativan order clonazepam tramadol online buy carisoprodol meridia online cheap valium cheap zoloft cyclobenzaprine online free sonyericsson ringtones flexeril online xanax online nokia ringtones valium online cheap wellbutrin sildenafil zoloft online cheap valium midi ringtones cheap cialis free qwest ringtones real ringtones real ringtones polyphonic ringtones tracfone ringtones cheap ultracet norco online polyphonic ringtones buy levitra cheap ambien cheap sildenafil prozac online xanax ortho online funny ringtones vicodin online cheap phentermine norco online buy lortab cheap hydrocodone ativan online buy paxil free mono ringtones valium online cheap tramadol cheap zoloft hydrocodone online cheap viagra cheap albuterol cheap rivotril free verizon ringtones lorazepam online cheap viagra cheap didrex phentermine online free cingular ringtones free ericsson ringtones free sony ericsson ringtones free motorola ringtones buy zanaflex flexeril online free samsung ringtones nokia ringtones cheap diazepam sonyericsson ringtones buy lorazepam free real ringtones but rivotril buy ambien cheap albuterol cialis online buy ambien sagem ringtones norco online meridia online cheap nexium free mono ringtones cheap hgh ultram online cheap ativan wellbutrin online free ringtones order diazepam free sprint ringtones lortab online carisoprodol online free verizon ringtones buy clomid xenical online xanax online free ringtones cheap wellbutrin buy carisoprodol free tracfone ringtones sharp ringtones buy ativan buy norco free sony ericsson ringtones free tracfone ringtones free sonyericsson ringtones soma online meridia online buy zyban clonazepam free music ringtones cyclobenzaprine online cheap ativan paxil online cheap ativan nexium mp3 ringtones cheap hgh buy propecia free samsung ringtones ortho online but zyban free punk ringtones cheap lipitor mtv ringtones vigrx online lorazepam online buy fioricet lorazepam online free cool ringtones free qwest ringtones diazepam online cheap tenuate clonazepam online buy fioricet ativan online buy clonazepam cheap propecia free mp3 ringtones buy hydrocodone cheap carisoprodol didrex online levitra online free nextel ringtones zyban online free mp3 ringtones levitra == Low-Level Tasks ==
From [Amar et al. 2005] and [Lee et al 2006].
- General Tasks
|Retrieve Value||Given a set of cases, find attributes of those cases.|
|Filter||Given some conditions on attributes values, find data cases satisfying those conditions.|
|Compute Derived Value||Given a set of data cases, compute an aggregate numeric representation of those data cases.(e.g. average, median, and count)|
|Find Extremum||Find data cases possessing an extreme value of an attribute over its range within the data set.|
|Sort||Given a set of data cases, rank them according to some ordinal metric.
Determine Range Given a set of data cases and an attribute of interest, find the span of values within the set.
|Characterize Distribution||Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute’s values over the set.|
|Find Anomalies||Identify any anomalies within a given set of data cases with respect to a given relationship or expectation, e.g. statistical outliers.|
|Cluster||Given a set of data cases, find clusters of similar attribute values.|
|Correlate||Given a set of data cases and two attributes, determine useful relationships between the values of those attributes.|
|Scan||Quickly review a set of items.|
|Set Operation||Given multiple sets of items, perform set operations on them. For example, find the intersection of the set of nodes.|
- Graph Specific Tasks
|Find Adjacent Nodes||Given a node, find its adjacent nodes.|
Graph Task Taxonomy
Examples are illustrated using 4 types of graphs:
- (FOAF): friend-of-a-friend
- (FW): food web
- (GO): gene ontology
- (ARM): airport routing map
|Adjacency (direct connection)||
|Accessibility (direct or indirect connection)||
|On the Nodes||
|On the Links||
This is a compound exploratory task to get estimated values quickly. For example, we might ask users to estimate the size of the social network. Note that sometimes it is more important to be able to estimate the answer than to get an accurate one. Some of the topology tasks can be done easily using an overview of the graph as well. For example, using particular layout algorithms, it is easy to see whether or not there are clusters and connected components. Other algorithms help to find shortest paths between nodes. Overview also helps to find patterns.
- estimate size of the network
- estimate the number of connected components
- is the network clustered?
- can you identify different patterns of connection?
- (FOAF) has the network a small-world structure?
High-Level tasks which are not a combination of lower level tasks.
- When we compare two or more food webs, we can ask the following questions: What do they have in common? What are the differences among those food webs? Is there any missing or conflicting information?
- Due to errors in the data, several nodes may represent the same entity. For example, the co-authorship graphs often have duplicate author nodes. One important task is to identify whether two or more nodes represent the same person.
- How has the graph changed over time?
There seems to be a set of tasks in the world that match very few of these, but show up often. I welcome others' ideas of how to categorize them:
"what is the general structure of this graph?" http://www.networkweaving.com/blog/2006/09/nola-networks.html
"what is the distribution of vertex degree in this graph?" (That is, "how are well-linked nodes different from under-linked nodes?") http://research.microsoft.com/research/pubs/view.aspx?type=Publication