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Algorithms and data structures for graph analysis

Algorithms and data structures
for graph analysis
Software implementations for graph structures can represent a graph as
an adjacency list or a sparse adjacency matrix. There are no standards
regarding the implementation details of adjacency list or adjacency matrix
structures. Notable parties propose different implementations for
adjacency lists [1]. Guido van Rossum suggests using a hash table with
arrays, where the arrays indicate edges to vertices. Cormen et al. [2]
suggest using an array of singly linked lists. Goodrich and Tamassia
propose dedicated objects for both vertices and edges, allowing for easily
adding information to both vertices and edges. Each of the
implementation suggestions has advantages and disadvantages. One can
easily come up with more variations, but their trade-offs in the context of
graph applications are not clear.
Typically the choice of implementation will be dominated by memory/ time
trade-offs and the need for dynamic graphs, i.e. the need to alter a graph
at random. The size of available datasets is ever increasing. Although bigdata environments allow for massive parallelization, not every graph
application can be parallelized; this puts these trade-offs more on the
foreground. Behind these observations there is a central question that is
relevant to the practice of graph theory: what is the price of using a
general framework for creating graph applications? Is it best to think of
designing a graph application in terms of a choice between one of the
general frameworks (NetworkX , SNAP, Jung, etc.) Or should one see the
design of a graph application more as a detailed choice of a set of
applicable data structures? A quick answer is: ‘trust the experts’. The
expert answer will of course always be: ‘it depends’. This question is
relevant for dedicated applications where more performance means more
competitive advantage.
In this project the impact of data structures on the performance of graph
applications is assessed. Both the impact on artificial graph generation
and the impact on several graph algorithms will be assessed. These two
steps provide a minimal workflow, applicable to many situations. More
specific the impact of data structures will be assessed for the following
 Synthetic graph generation using a nearest neighbor algorithm [ref
 Spectral clustering of a graph [3]
 Calculating maximum flows using the Ford-Fulkerson
Document overview
This document contains the following sections:
 A critique motivating the research subject
 A short overview of relevant data structures with some
 A rudimentary performance measurements of relevant data
structures in the context of nearest neighbors
 A memory optimized data structures for graphs
 A comparison of three data structures in a graph context
 Performance measurements of synthetic graph generation using a
nearest neighbor approach
 Performance measurements of a spectral clustering algorithm using
different data structures
 Performance measurements of the Ford-Fulkerson algorithm using
different data structures
A review of major graph implementations shows that implementation
decisions regarding data structures are not made explicit. NetworkX [ref
website], an elaborate Python based framework, does not mention
implementation decisions regarding graph data structures; nor do SNAP
[4] or Jung [5]. The primary reason for this is likely to be the fact that all
these frameworks are positioned as general frameworks. The qualification
of general in this context can be interpreted as ‘flexible’ or ‘broad, without
specific aim’. The frameworks aim to offer the broadest support for graph
applications, including dynamic graphs. The inclusion of dynamic graphs
most likely precludes (or not?) the application of for example static arrays.
A more flexible data structure for storing nodes like a maps or ordered
maps seems more likely. But then the representation of edges remains
open: linked lists, or dynamic lists, like Python lists or C++ vectors, or also
a map or a set?
In computer science the performance characteristics of different data
structures are well known and stressed. Performance characteristics of
specific algorithms like for example breadth first search or depth first
search are also generally available. The performance trade-offs of data
structures in the context of graph applications seem less considered, and
therefore less known. The choice of data structure is of course impacted
by graph algorithm under consideration; but then most graph applications
are written in the context of general frameworks: this completes a circle.
As noted before, the trade-offs between data structures become more
relevant as the size of available graph datasets grows, and the interests in
a competitive advantage are growing. Even if a graph algorithm itself is
not parallelizable, one could for example tap into the power of map reduce
frameworks to pre-sort datasets, off-loading parts of non-critical work; this
is often done in information retrieval work flows. This project aims to
assess the trade-offs between data structures for graph applications, with
a bias towards applications for larger data sets.
First an overview of relevant properties for graph data structures is given.
Relevant properties for graph data structures
The following general properties can be listed as relevant for graph data
 Fast random access to nodes and edges
 Fast iteration over nodes and edges
 The ability to add and remove nodes and edges
 Memory efficiency
Typically one would like to lookup items in constant O(1) or O(log n) time.
Iteration allows for visiting nodes or edges in a single sequence without
random access, for example in an iteration context. The ability to remove
nodes and edges could be optional in some situations, but should
generally be supported. Regarding memory efficiency three notes can be
• Representing a node ('0') as the first element in a list and deleting
that node would result in node '1' being represented as '0'; this is
not favorable. One would require 'sparseness'.
• Storing edges outside the context of
• To efficiently remove nodes for an undirected graph both outgoing
and incoming edges should be accessible fast (i.e. fast row and
column access)
With the properties above in mind three interesting scenarios emerge:
 Which design is best if optimizing for memory?
 Which design is best if optimizing for adding and removing nodes
and edges?
 What are the speed trade-offs?
These topics will be investigated in the next sections. First an overview of
candidates for node and edges data structures will be given.
A short overview of relevant data structures
In this section the following six relevant data structures are reviewed:
 (Sparse) matrix
 Linked list
 Indexable skip list
 Dynamic array (vector)
 Ordered map (tree backed)
 Unordered map (hash table backed)
 Judy array
This overview is not intended to be exhaustive, but should cover enough
ground. At the end of this section an overview of some key properties of
these data structures is given. The data structures will be shortly reviewed
in the next sections.
Full matrices provide a lot of good properties as a data structure for
graphs: fast insertion, lookup and deletion. Traversing the (non-zero)
elements of a row or column though is not so fast since all elements have
to be visited. In practice the high memory requirements often prevents
application of a full matrix. Sparse matrices circumvent the drawbacks of
matrices, at a cost of course. Sparse matrices, like adjacency lists, do not
have a fixed implementation. The requirements for sparse matrices mimic
the requirements for graph data structures, as mentioned in the previous
section (notably fast row and column access). Any graph data structure
will in a way mimic a sparse matrix data structure; because of this the
properties of sparse matrices will not be assessed to avoid a recursion of
the questions at hand. Implementations of sparse matrices can be based
on dictionaries of dictionaries, lists of list, coordinate lists (lists with (x, y)
or (x, y, value) tuples). The last representation is popular in map/reduce
A linked list is a chain of value and pointer tuples. Linked lists readily
support fast iteration and insertion. Storing the values and the pointers
requires extra memory. Since random access is not supported, lookups and
deletions require traversal. For this reason ordering the items in the list for
example does not help lookup.
Skip lists try to fix these drawbacks. Skip lists add additional pointers to
values farther down the chain. Lookups can skip large segments of items
by ‘jumping’ over them. Adding multiple forward options to elements down
the chain can mimic a binary search structure. By adding the number of
skipped items per jump, additionally random access of elements by index
is possible. This structure is only introduced because of its fit for one the
experiments (graph generation).
A dynamic array is range of continuous memory, containing elements of a
specific type (integer, double, etc.). Contrary to a linked list, a dynamic
array allow for index lookup, and allow for a binary search in ordered
items. Key point is that deleting items requires moving large blocks of
An ordered set or map places elements in a binary tree structure. Lookup,
insertion and deletion are reasonable. The tree structure requires a lot of
memory Elements can be iterated over in order. A hash map stores
elements in an array structure based on the hash value of the element.
Collision can be resolved in multiple ways. The elements can not be
accessed in order.
Judy arrays apply tries to save memory. The design is very complex. I
Imagine an n-ary tree where the intermediate (integer) values are woven
into a trie.
Data structure
Linked list
Linked list (ordered)
Indexable skip list
Dyn. array
Dyn. array (ordered)
Ordered map (tree)
Unordered map
Judy arrays
24 bytes
O(log n)
O(log n)
24 bytes
32 bytes
O(log n)
O(log n)
O(log n)
24 bytes
24 bytes
Exp. O(1)
40 bytes
24 bytes
In the next sections some rudimentary performance measurements of
relevant data structures is presented.
Rudimentary performance measurements of
relevant data structures
Rudimentary speed and memory characteristics of the previously
mentioned data structures are assessed in this section. The following
characteristics are assessed:
• Memory consumption (figure 1)
• Creation speed of data structure (figure 2)
• Iteration speed over data structure (figure 3)
• Lookup speed of random elements in data structure (figure 4)
• Deletion speed of elements in data structures (figure 5)
During measurements sets were used rather than maps to accentuate the
overhead of index structures rather than extra dictionary values. This was
not possible for Judy arrays.
During the experiments the C++ wrapper of Judy arrays required lots of
debugging. Creating a larger amount of Judy arrays required a lot of
memory. For this reason Judy arrays were dropped.
As an alternative to a dynamic array a deque was added to the
measurements. A deque supports fast addition of elements to the front
and back; this is made possible by not storing the elements in contiguous
memory, but in contiguous blocks. This property does not apply in the
context of this study, further considerations is drop.
Sparse and dense maps and sets as implemented by Google were added
to the measurements in a later stage. These data structures use bins, like
hash maps and groups. Time was not available to investigate the
structures in more detail (this is swapped for other activities). The ability
to reserve memory upfront indicates that that some type of array structure
is involved (probably involving groups/bin of length magic number 48).
Lookups in dynamic arrays (and deques) are based on a binary search in
ordered values, not on index access due to reasons of sparseness. Two
compare the creation of ordered dynamic arrays using insertion sort and
post sort, an additional measurement was performed (figure 6).
Due to brevity reasons the results will not be discussed. Based on the
results two candidate data structures will be presented in the next
Figure 1 Memory consumption
Figure 2 Creation speed of data structures
Figure 3 Iteration speed of data structures
Figure 4 Lookup speed of data structures
Figure 5 Deletion speed of 10000 random entries
Figure 6 Creation of ordered dynamic arrays (dense set added for
For this study the following tools were used:
Ubuntu 14.04
Workstation Xeon 12 core, 2.7 Ghz, 24Gb RAM, 6Gbps disk
A memory optimized candidate for data
This section is dense as it involves the synthesis of a lot of information. For
reasons of simplicity directed graphs are considered. First the memory
optimized candidate is presented, second a short note on a read/write
optimized candidate.
Memory optimized candidate
As the previous measurements show, dynamic arrays are efficient in
storing data; as efficient as it gets. Some initial observations are:
• In the memory measurements there seems to be some memory
overhead, this is likely some fragmentation that is not immediately
cleaned up; it will be ignored.
• Node id-s can be assumed continuous (ignoring deletions) as it can
be deferred to off-line processing.
• Insertions into and deletions from vectors of moderate length
(10.000 – 100.000) is still moderately possible (this range is what
one might call 'Pythonistan')
Using primitive types to store data in dynamic arrays prevents C++
object structure overhead
If power laws are involved then using a separate vector to store the
edges of a node might present unwanted overhead.
The 'sparseness' argument requires at least storage of the node id-s
itself apart from storing the edges.
If storing very large graphs the allocation of very large contiguous
memory might cause memory management issues.
For the candidate deletion of nodes and edges is not considered
optional, since this would limit the application space.
Applying variable byte encoding like in search application is not
favorable since indexed access is required
The sparse hash map can store elements with an overhead of one bit
Lookups in sorted vectors using binary search is relatively slow
Based on the observations above, the following progression of memory
optimized data structures can be made.
A simple data structure would store the node id-s in an array, and store
the edges of the nodes in separate arrays (one per node, the edges are
sorted), accessible via a central array of pointers to those arrays. If
creating a vector per node for the edges is to much overhead then the
following progression can be made.
The edges are not stored in separate arrays, but are stacked by order of
node id in a single array. Since this could result in a huge array for the
edges, a further progression can be made to help memory management.
Based on the node id-s bins are made using the modulo operator. The
node id-s are stored in sorted arrays, one per bin. These arrays are
accessible via an array of pointers, which length is based on the modulo
argument. The edges are stored sorted in a separate array per bin and
stacked in order of the node id-s. A third array (per bin) stores the starting
index of the nodes edges (i.e. the third node in the bin is '5', the edges of
which start at index '45' of the edge array). Since nodes have to be looked
up in the node arrays using binary search, further refinement is needed, as
the measurements indicate.
Instead of using a separate array for the node id's and the edge indexes, a
single sparse hash map (SHM) can be used to store both the node id-s and
the edge index at once. As the measurements show, this is fast and
efficient. Additionally the edge array can be discarded. Since internally the
nodes in the sparse hash map are not stored continuously, binning is not
necessary for nodes. The edge array though can still be broken down in
bins: this is likely to be the largest structure.
Insertion of nodes would require appending to the sparse map; this should
not be an issue.
Insertion of edges would require updating the array with the edge indices,
since the the edges are stacked per node. This is not efficient. But in one
case though:
• assuming more or less continuous node id-s
• assuming a moderate range of edge degrees
• setting the modulo argument to the number of nodes
it is possible to land in 'Pythonistan' since every node has it own edge list.
On appending a node:
• the modulo argument has to be incremented
• an extra edge edge array has to be created
This is not an ideal situation, but might be manageable.
Deletion requires a deletion from the node in the sparse map (fast as the
measurements show) and a deletion from the edge arrays. Deletions from
the edge array could be supported reasonably if the edge array is stored in
in sparse hash map too and the reverse edges are stored for fast incoming
edge lookups. This would probably need a 'landing edge' that is never
deleted for edges in the edge array. Random edges lookup can not use
binary search to lookup random edges if the edges are stored in a sparse
map; this possible if each node has a separate sparse hash set: this
requires more memory but might be an option for the speed optimized
The resulting data structure allows optimization under several
circumstances and supports a wide range of applications. In coming
section the properties of this structure will be assessed. Interestingly the
structure is some form of a map of maps.
Speed optimized candidate
Additionally the notion of speed is less uniform than the notion of memory.
In light of the results in the previous section, the rest of this project the
memory optimized candidate will be compared to SNAP instead, making
adjustments if necessary.
A comparison of data structures in a graph
In this section the memory optimized candidate (MOC) is benchmarked to
SNAP for several graph related tasks. First a table of results is presented,
followed by some comments. The measurements are intended to test the
data structure requirements as stated earlier, and might reflect general
best practices.
Read data set
1 bin
2^14 bin
Lookup 1M
random nodes
Nodes in SHM
Power iteration
+300 MB
Native using
1 bin
Remove 1M
14^2 bins
Create BFS tree
The datasets used were taken from SNAP [5]:
• LiveJournal social network (4847571 nodes/68993773 edges)
Since the MOC does not build up any binary tree structures (BTS) in
memory for storing the nodes, reading datasets is very fast. Two
preprocessing tasks are required though for this speed:
• a list of unique node id-s with edge counts (a preprocessing task)
• the input file consists of edges sorted per node (a preprocessing
Note that the graph is directed and that no reverse edges are stored.
In the MOC memory measurement the measured quantity was DRS
The power iteration shows that the MOC is fast enough iteration wise. Note
that the SNAP power iteration was a simple one, created for this
measurement. The SNAP power iteration iterated over the edges, the MOC
power iteration iterated over the nodes first. This is a necessary step that
does not seem to hurt. Native Lanczos in SNAP is faster.
Removing edges is not implemented due to time contstraints.
The creation of the BFS tree can be done fast in MOC by avoiding the
usage of set like structures. First an appropriate empty node and edge
structure is copied were the node id-s are marked using a special marker
(MAX_INT for example). With the sparse representation this is not too bad,
it requires about as much memory as a set containing a 95% SCC (as can
be concluded from the measurements, and there might be some logic
behind this), but it is considerably faster. Next during the BFS each marker
is replaced by the number of valid outgoing edges, if the node is visited. If
a node has been visited, the edge is marked (MAX_INT for example),
indicating is has been surpassed. Finally the node structure is compacted.
Intermediary notice
Up to this point the project has been updated after the milestone. The next
results have not changed since the milestone, and are presented as is; the
conclusion exempt.
Performance measurements of synthetic graph
In this section the performance characteristics of synthetic graph
generation are assessed. To generate the data a nearest neighbor
approach as described by [ref] is used. A critical part of the generation
process involves randomly choosing a potential connection from a large
set of potential connections. Two strategies are explored: storing all
potential edges in one set (‘global’), and storing potential edges as ‘local’
sets connected to the ‘central’ node of the potential connections. In light
of the previous measurements, nodes are stored as elements of vectors
since during graph creation nodes are ‘append’ only. For storing the
potential connections random access skip lists are used, since this
structure seems to fit the requirements best. This might come with a hefty
memory penalty. Note that although maps allow for faster lookups and
deletions, these structures do not allow for indexed lookup, which is
favorable for random draws.
In the simulations u=0.5 and k=1.
Outside the moderate range, memory/speed trade offs are unavoidable,
the choice of data structures have a big impact on the performance of the
graph generation algorithm. The trade-off to be made would likely be
between random access skip lists for speed and vectors for memory
efficiency. But this only relevant if the number of potential edges exceeds
the moderate range; for a lot of cases using for example vectors to store
potential connections can suffice. Using more specific knowledge of u one
might be able to optimize memory requirements by using a local strategy
and storing either the unconverted potential connections or the
Performance measurements of spectral clustering
One can apply spectral clustering using the power method to find the
eigenvectors of a Laplacian matrix representation of a graph. Let it first be
noted that map reduce is specifically fit to perform power iterations over
large graphs. This fact greatly diminishes the applicability of performance
assessments in this section.
In this assessment the NJW [3] method will applied, using the largest
eigenvector. After the eigenvectors are found, the nodes are clustered
using a k-means algorithm initialized with kmeans++.
The nodes are stored as vectors. For the iteration of edge values one must
then store both a column index and the value itself. Four implementation
options seem valid for storing the edges:
 A linked list of column-value pairs
 Two parallel linked lists with column and values
 A dynamic array of column-value pairs
 Two parallel dynamic arrays with column and values
A very small traversing experiment shows that the last structure is the
dominant choice. i.e. both speed and memory wise. Since maps also
allow for iterator access, the performance of maps is considered equal
to the performance of a linked list with objects. This should ne
comparable to the single linked list implementation; the performance of
which is comparable to the data structure of choice. Hence, a maps-ofmaps generic graph structure is likely to yield similar performance
(requiring more memory of course as shown earlier).
Crunching two parallel dynamic arrays set-up a bit more, the final data
structure for the assessment contains just three lists:
 One list with the cumulative number of edges over the nodes
 One list with block wise the edges per node (addressable using
the first list)
 One list with block wise the values per edge in the same order as
the second list.
A power iteration now involves the iteration over three consecutive
memory blocks. This sequential pattern shows that the computation can
be performed parallel, using for example map/ reduce. A similar graph
generation set-up is used as in the previous section.
Performance measurements Ford-Fulkerson
In this section the impact of data structures on the performance of the
Ford-Fulkerson maximum flows algorithm is assessed. Unlike the algorithm
in the previous section, this algorithm is not as easily parallelized; which
provides an opportunity for improvements.
Two different implementations of the algorithm will be compared. The first
version uses as data structure a map of maps to represent a weighted
graph, the second version uses the compact data structure with three
vectors as mentioned in the previous graph. The algorithm requires the
random access on the edges of a node. As shown at the beginning, one
would expect maps to be faster.
Maps of maps
Three vectors
A non-representative micro experiment shows that solving a small 6 nodes
flow network 10000 times is actually done faster by the graph with three
vectors. Clearly the seek time to assess the edges weights is not
dominating the time taken yet. The probability that this result is real is
very low. Even if proven valid, generalizing the results should be done with
utter care.
In the introduction it was stated that textbook implementations leave
ground uncovered. In this project it was shown that newer data structures
(notably from Google) offer different possibilities, that allow for the design
of new solutions. These designs are relevant in contexts were performance
delivers an competitive advantage.
It was shown that dynamic arrays do a decent job in the moderate range;
dynamic arrays must have been very well optimized on all levels. The
point above explains much of the success of Python and its list structures.
It seems that there new opportunities in ‘extremistan’ trade-offs are hard
and shall be made consciously.
C++ turned out a good type of environment for this kind of assessment;
though implementing software in C++ is not trivial. The information
presented in this context is not new; accessing this information requires
extensive search, and evaluation.
Project statistics
C++ code: 1400-1600 lines
Python POC code: similar to milestone (600-700?)
Gnuplot code: 200-300 lines
A short note on a bumpy road
These project results are at the end of a bumpy road in many ways.
Implementing software in C++ proved hard; harder than in Python for
example. C++ might not have been the right choice productivity wise. The
language does provide opportunities, as was hopefully shown.
Looking back at the midterm expectations I can conclude the following.
Implementing the software so far proved harder than expected. But finding
new technical opportunities and molding these into a design is hard,
especially of the beaten track. I had a hunch of this, but not strong
enough. I underestimated the time involved for the extra requirements as
presented by the TA. These requirements fall more into the space of graph
analysis itself. I can see the reasons for the requirements, and would have
liked meeting those. This should in my opinion not diminish the value of
the hard work put in and the results: this is the central question though.
This is my seventh course at Stanford. Compared to other courses, I put in
as much time for this course as for the other courses. To make progress
with the project (and for personal reasons), I took more than four weeks
leave, and even ended up terminating my contract. As stated, a bumpy
road. Time ended up being available by setting priorities. From my part I
am happy to have taken the challenge. Compared to the other courses, in
this course I reached far to get into more serious space regarding bigger
data crunching.
[1] “Adjacency list” Wikipedia. Wikimedia Foundation, accessed 10
december 2014.
[2] Thomas H. Cormen, Clifford Stein, Ronald L. Rivest, and Charles E.
Leiserson. Introduction to Algorithms(2nd ed.). McGraw-Hill Higher
Education, (2002).
[3] Ng, A., Jordan, M., Weiss, Y.. On spectral clustering: analysis and an
algorithm. In: Dietterich, T., Becker, S., Ghahramani, Z. (eds.) Advances in
Neural Information Processing Systems 14, pp. 849- 856. MIT Press,
Cambridge (2002).
[4] Leskovec, J. Stanford analysis project. Stanford University, accessed 10
december 2014.
[5] “Java Universal Network/Graph Framework”., accessed
10 december 2014.
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