The maximum clique enumeration problem: algorithms, applications, and implementations
 John D Eblen^{1},
 Charles A Phillips^{2},
 Gary L Rogers^{2} and
 Michael A Langston^{2}Email author
https://doi.org/10.1186/1471210513S10S5
© Eblen et al.; licensee BioMed Central Ltd. 2012
Published: 25 June 2012
Abstract
Background
The maximum clique enumeration (MCE) problem asks that we identify all maximum cliques in a finite, simple graph. MCE is closely related to two other wellknown and widelystudied problems: the maximum clique optimization problem, which asks us to determine the size of a largest clique, and the maximal clique enumeration problem, which asks that we compile a listing of all maximal cliques. Naturally, these three problems are $\mathcal{N}\mathcal{P}$hard, given that they subsume the classic version of the $\mathcal{N}\mathcal{P}$complete clique decision problem. MCE can be solved in principle with standard enumeration methods due to Bron, Kerbosch, Kose and others. Unfortunately, these techniques are illsuited to graphs encountered in our applications. We must solve MCE on instances deeply seeded in data mining and computational biology, where highthroughput data capture often creates graphs of extreme size and density. MCE can also be solved in principle using more modern algorithms based in part on vertex cover and the theory of fixedparameter tractability (FPT). While FPT is an improvement, these algorithms too can fail to scale sufficiently well as the sizes and densities of our datasets grow.
Results
An extensive testbed of benchmark graphs are created using publicly available transcriptomic datasets from the Gene Expression Omnibus (GEO). Empirical testing reveals crucial but latent features of such highthroughput biological data. In turn, it is shown that these features distinguish real data from random data intended to reproduce salient topological features. In particular, with real data there tends to be an unusually high degree of maximum clique overlap. Armed with this knowledge, novel decomposition strategies are tuned to the data and coupled with the best FPT MCE implementations.
Conclusions
Several algorithmic improvements to MCE are made which progressively decrease the run time on graphs in the testbed. Frequently the final runtime improvement is several orders of magnitude. As a result, instances which were once prohibitively timeconsuming to solve are brought into the domain of realistic feasibility.
Background
A clique is a fullyconnected subgraph in a finite, simple graph. The problem of determining whether or not a graph has a clique of a given size, called simply CLIQUE, is one of the best known and most widely studied combinatorial problems. Although classically formulated as an $\mathcal{N}\mathcal{P}$complete decision problem [1], where one is merely asked to determine the existence of a certain size clique, the search and optimization formulations are probably most often encountered in practice, where one is asked to find a clique of given size and largest size respectively. In computational biology, one needs to look no farther than PubMed to gauge clique's utility in a variety of applications. A notable example is the search for putative molecular response networks in highthroughput biological data. Popular cliquecentric tools include clique community algorithms for clustering [2] and paracliquebased methods for QTL analysis and noise abatement [3, 4].
A clique is maximal if it cannot be augmented by adding additional vertices. A clique is maximum if it is of largest size. A maximum clique is particularly useful in our work on graphs derived from biological datasets. It provides a dense core that can be extended to produce plausible biological networks [5]. Other biological applications include the thresholding of normalized microarray data [6, 7], searching for common cisregulatory elements [8], and solving the compatibility problem in phylogeny [9]. See [10] for a survey of additional applications of maximum clique.
Any algorithm that relies on maximum clique, however, has the potential for inconsistency. This is because graphs often have more than just one maximum clique. Idiosyncrasies between algorithms, or even among different implementations of the same algorithm, are apt to lead to an arbitrary choice of cliques. This motivates us to find an efficient mechanism to enumerate all maximum cliques in a graph. These can then be examined using a variety of relevant criteria, for example, by the average weight of correlations driven by strain or stimulus [11].
We therefore seek to solve the Maximum Clique Enumeration (MCE) problem. Unlike maximal clique enumeration, for which a substantial body of literature exists, very little seems to be known about MCE. The only exception we have found is a gametheoretic approach for locating a predetermined number of largest cliques [12].
While very little prior work seems to have been done on MCE, the problem of maximal clique enumeration has been studied extensively. Since any algorithm that enumerates all maximal cliques also enumerates all maximum cliques, it is reasonable to approach MCE by attempting first to adapt existing maximal clique enumeration algorithms. An implementation of an existing maximal clique enumeration algorithm also provides a useful runtime benchmark that should be improved upon by any new approach. Besides maximal clique enumeration algorithms, another potential strategy is to compute the maximum clique size and then test all possible combinations of vertices of that size for connectivity. While this approach may be reasonable for very small clique sizes, as the maximum clique size increases the runtime quickly becomes prohibitive, and we mention it only for completeness, and focus our efforts on modifying and extending existing algorithms for enumerating maximal cliques.
Many variations of backtracking algorithms for maximal clique enumeration have been published in the literature. To the best of our knowledge, all can be traced back to the algorithms of Bron and Kerbosch first presented in [15]. Some subsequent modifications tweak the data structures used. Others change the order in which vertices are traversed. See [16] for a performance comparison between several variations of backtracking algorithms. As a basis for improvement, however, we implemented the original, highly efficient algorithm of [15]. We made this choice for three reasons. First, an enormous proportion of the time consumed by enumeration algorithms is spent in outputting the maximal cliques that are generated. This output time is a practical limitation on any such approach. Second, a graph can theoretically contain as many as 3^{(n/3)}maximal cliques [17]. It was shown in [18] that the algorithm in [15] achieves this bound in the worst case. No algorithm with a theoretically lower asymptotic runtime can thus exist. Third, and most importantly, the improvements we introduce do not depend on the particulars of any one backtracking algorithm; they can be used in conjunction with any and all of them.
Results and discussion
Using the seminal maximal clique enumeration algorithm due to Bron and Kerbosch [15] as a benchmark, we designed, implemented, and extensively tested three algorithmic improvements, the last based on observations about the nature of graphs produced by transcriptomic data. Along with describing these improvements, we will describe our existing tool for finding a single maximum clique, based on the theory of fixedparameter tractability (FPT) [19, 20]. Such a tool is essential for all three improvements, since the first two rely on knowledge of the maximum clique size, and the last uses the maximum clique finding tool as a subroutine. All codes are written in C/C++ and compiled in Linux. For testing, we use 100 graphs derived from 25 different datasets which are publicly available on GEO. We concentrate on transcriptomic data, given its abundance, and eschew synthetic data, having learned long ago that effective algorithms for one have little bearing on the other. (The pathological matchings noted in [21] for vertex cover can be extended to clique, but likewise they too are of course hugely irrelevant to real data.) In an effort to improve performance, we scrutinize the structure of transcriptomic graphs and explore the notion of maximum clique covers and essential vertex sets. Indeed, we find that with the right preprocessing we are able to tailor algorithms to the sorts of data we routinely encounter, and that we can now solve instances previously considered unassailable.
Algorithms
In the following sections, we describe each of the MCE algorithms we implemented and tested. The first is the algorithm of Bron and Kerbosch [15], which we call Basic Backtracking and use as a benchmark. Since all our subsequent improvements make use of an algorithm that finds a single maximum clique, we next describe our existing tool, called Maximum Clique Finder (MCF), which does just that. We next modify the Basic Backtracking algorithm to take advantage of the fact that we only want to find the maximum cliques and can quickly compute the maximum clique size. We call this approach Intelligent Backtracking, since it actively returns early from branches that will not lead to a maximum clique. We then modify MCF itself to enumerate all maximum cliques, an approach we call Parameterized Maximum Clique, or Parameterized MC. In a sense this is another backtracking approach that goes even further to exploit the fact that we only want to find maximum cliques. Finally, based on observations about the properties of biological graphs, we introduce the concepts maximum clique covers and essential vertex sets, and apply them to significantly improve the runtime of backtracking algorithms.
Basic backtracking
The seminal maximal clique publication [15] describes two algorithms. A detailed presentation of the second, which is an improved version of the first, is provided. It is this second, more efficient, method that we implement and test. We shall refer to it here as Basic Backtracking. All maximal cliques are enumerated with a depthfirst search tree traversal. The primary data structures employed are three global sets of vertices: COMPSUB, CANDIDATES and NOT. COMPSUB contains the vertices in the current clique, and is initially empty. CANDIDATES contains unexplored vertices that can extend the current clique, and initially contains all vertices in the graph. NOT contains explored vertices that cannot extend the current clique, and is initially empty. Each recursive call performs three steps:

Select a vertex v in CANDIDATES and move it to COMPSUB.

Remove all vertices not adjacent to v from both CANDIDATES and NOT. At this point, if both CANDIDATES and NOT are empty, then COMPSUB is a maximal clique. If so, output COMPSUB as a maximal cique and continue the next step. If not, then recursively call the previous step.

Move v from COMPSUB to NOT.
Note that NOT is used to keep from generating duplicate maximal cliques. The search tree can be pruned by terminating a branch early if some vertex of NOT is connected to all vertices of CANDIDATES.
Vertices are selected in a way that causes this pruning to occur as soon as possible. We omit the details since they are not pertinent to our modifications of the algorithm.
The storage requirements of Basic Backtracking are relatively modest. No information about previous maximal cliques needs to be retained. In the improvements we will test, we focus on speed but also improve memory usage. Thus, such limitations are in no case prohibitive for any of our tested methods. Nevertheless, in some environments, memory utilization can be extreme. We refer the interested reader to [14].
Our Basic Backtracking implementation serves as an initial benchmark upon which we can now try to improve.
Finding a single maximum clique
We use the term Maximum Clique Finder (MCF) to denote the software we have implemented and refined for finding a single clique of largest size [22]. MCF employs a suite of preprocessing rules along with a branching strategy that mirrors the wellknown FPT approach to vertex cover [19, 23]. It first invokes a simple greedy heuristic to find a reasonably large clique rapidly. This clique is then used for preprocessing, since it puts a lower bound on the maximum clique size. The heuristic works by choosing the highest degree vertex, v, then choosing the highest degree neighbor of v. These two vertices form an initial clique C, which is then iteratively extended by choosing the highest degree vertex adjacent to all of C. On each iteration, any vertex not adjacent to all of C is removed. The process continues until no more vertices exist outside C. Since C is a lower bound on the maximum clique size, all vertices with degree less than C  1 can be permanently removed from the original graph. Next, all vertices with degree n  1 are temporarily removed from the graph, but retained in a list since they must be part of any maximum clique. MCF exploits a novel form of color preprocessing [22], used previously in [24] to guide branching. This form of preprocessing attempts to reduce the graph as follows. Given a known lower bound k on the size of the maximum clique, for each vertex v we apply fast greedy coloring to v and its neighbors. If these vertices can be colored with fewer than k colors, then v cannot be part of a maximum clique and is removed from the graph. Once the graph is thus reduced, MCF uses standard recursive branching on vertices, where each branch assumes that the vertex either is or is not in the maximum clique.
Intelligent backtracking
Paramaterized enumeration
Given that MCF employs a vertex branching strategy, we investigated whether it could be modified to enumerate not just one, but all maximum cliques. It turns out that MCF, also, lends itself to a straightforward modification that results in enumeration of all maximum cliques. The modification is simply to maintain a global list of all cliques of the largest size found thus far. Whenever a larger maximum clique is found, the list is flushed and refreshed to contain only the new maximum clique. When the search space has been exhausted, the list of maximum cliques is output.
We must take special care, however, to note that certain preprocessing rules used during interleaving are no longer valid. Consider, for example, the removal of a leaf vertex. The clique analogue is to find a vertex with degree n  2 and remove its lone nonneighbor. This rule patently assumes that only a single maximum clique is desired, because it ignores any clique depending on the discarded vertex. Therefore this particular preprocessing rule must be omitted once branching has begun.
Maximum clique covers
If we view MCF as a black box subroutine that can be called repeatedly, it can be used in a simple greedy algorithm for computing a maximal set of disjoint maximum cliques. We merely compute a maximum clique, remove it from the graph, and iterate until the size of a maximum clique decreases. To explore the advantages of computing such a set, we introduce the following notion:
Definition 1 A maximum clique cover of G = (V, E) is a set V' ⊆ V with the property that each maximum clique of G contains some vertex in the cover.
The union of all vertices contained in a maximal set of disjoint maximum cliques is of course a maximum clique cover (henceforth MCC), because all maximum cliques must overlap with such a set. This leads to a useful reduction algorithm. Any vertex not adjacent to at least one member of an MCC cannot be in a maximum clique, and can thus be removed.
In practice, we find that applying MCC before the earlier backtracking algorithms yields only marginal improvement. The concept of MCC does, however, lead to a much more powerful approach based on individual vertices. Since any improvement made by MCC is subsumed by the next approach, we do not test MCC by itself.
Essential vertex sets
Our investigation of the MCC algorithm revealed that it typically does not reduce the size of the graph more than the preprocessing rules already incorporated into MCF. For example, MCF already quickly finds a lower bound on the maximum clique size and removes any vertex with degree lower than this bound. Upon closer examination, however, we found that for 74 of 75 graphs that we initially tested for the conference version of this paper, only one clique was needed in an MCC. That is to say, one maximum clique covered all other maximum cliques. And in our current testbed of 100 graphs, in every case a single maximum clique suffices for an MCC. In fact this coincides closely with our experience, in which we typically see high overlap among large cliques in the transcriptomic graphs we encounter on a regular basis. Based on this observation, we shall now refine the concept of MCC. Rather than covering maximum cliques with cliques, we cover maximum cliques with individual vertices.
We define an essential vertex as one that is contained in every maximum clique. Of course it is possible for a given graph to have no such vertex, even when it contains many overlapping maximum cliques. But empirical testing of large transcriptomic graphs shows that an overwhelming number contain numerous essential vertices. And for purposes of reducing the graph, even one will suffice. An essential vertex has the potential to be extremely helpful, because it allows us to remove all its nonneighbors. We employ the following observation: for any graph G, ω(G) >ω(G/v) if and only if v covers all maximum cliques, where ω(G) is the maximum clique size of G.
Implementation
We implemented all algorithms in either C or C++. The code was compiled using the GCC 4.4.3 compiler on the Ubuntu Linux version 10.04.2 operating system as well as the GCC 3.3.5 compiler under Debian Linux version 3.1. All timings were conducted in the latter Debian environment on dedicated nodes of a cluster to ensure no affect on timings from concurrent processes. Each node had a dualcore Intel Xeon processor running at 3.20 GHz and 4 GB of main memory.
Testing
In the conference version of this paper, we used three different datasets at 25 thresholds each to derive a total of 75 graphs on which to test our algorithmic improvements. While these graphs certainly sufficed as an initial proof of concept, two concerns could be raised regarding them. First, one might argue that three datasets are not a sufficiently large sample size to provide a true sense of the overall nature of transcriptomic data or an algorithmic improvement's general effectiveness on such data, the large number of thresholds notwithstanding. And second, since the three datasets are proprietary and not publicly available, the results were not as readily reproducible as they might otherwise have been. Obtaining deidentified versions, while feasible, was an unnecessary obtacle to reproducibility.
GEO Datasets Used for Testing
DataSet  Title  Organism 

GDS3505  Seedling roots response to auxin and ethylene availability  Arabidopsis thaliana 
GDS3521  Retina response to hypoxia and subsequent reoxygenation: time course  Mus musculus 
GDS3538  Age and diet effect on canine skeletal muscles  Canis lupus familiaris 
GDS3561  Occupational benzene exposure: peripheral blood mononuclear cells (HumanRef8)  Homo sapiens 
GDS3579  Fer1 null mutants  Caenorhabditis elegans 
GDS3592  Ovarian normal surface epithelia and ovarian cancer epithelial cells  Homo sapiens 
GDS3595  Macrophage response to H1N1 and H5N1 influenza viral infections  Homo sapiens 
GDS3603  Renal cancer response to rapamycin analog CCI779 treatment:  Homo sapiens 
GDS3605  Spared nerve injury model of peripheral neuropathic pain: dorsal horn of spinal cord  Rattus norvegicus 
GDS3610  Nasopharyngeal carcinoma  Homo sapiens 
GDS3622  Nrf2deficient lung response to cigarette smoke: dose response and time course  Mus musculus 
GDS3623  Heart regeneration in zebrafish  Danio rerio 
GDS3639  Male and female fruit flies of various wildtype laboratory strains  Drosophila melanogaster 
GDS3640  Copper effect on liver cell line: dose response and time course  Homo sapiens 
GDS3644  Cerebral palsy: wrist muscles  Homo sapiens 
GDS3646  Celiac disease: primary leukocytes  Homo sapiens 
GDS3648  Cardiomyocyte response to various types of fatty acids in vitro  Rattus norvegicus 
GDS3661  Hypertensive heart failure model  Rattus norvegicus 
GDS3672  Hypertension model: aorta  Mus musculus 
GDS3690  Atherosclerotic Coronary Artery Disease: circulating mononuclear cell types  Homo sapiens 
GDS3715  Insulin effect on skeletal muscle  Homo sapiens 
GDS3716  Breast cancer: histologically normal breast epithelium  Homo sapiens 
GDS3703  Addictive drugs effect on brain striatum: time course  Mus musculus 
GDS3707  Acute ethanol exposure: time course  Drosophila melanogaster 
GDS3692  Lean B6.CD7Mit353 strain: various tissues  Mus musculus 
From the expression data, we first constructed weighted graphs in which vertices represented probes and edge weights were Pearson correlation coefficients computed across experimental conditions. We then converted the weighted graphs into unweighted graphs by retaining only those edges whose weights were at or above some chosen threshold, t. For each dataset, we chose four values for t. All size/density values were within the spectrum typically seen in our work with biological datasets. The smallest graph had 3,828 vertices and 310,380 edges; the largest had 44,563 vertices and 2,052,228 edges.
For each algorithm on each graph, we conducted timings on a dedicated node of a cluster to avoid interference from other processes. If the algorithm did not complete within 24 hours, it was halted and the graph was deemed to have not been solved. We chose thresholds to spread the runtimes of the graphs out over the five algorithms we were testing. The largest (smallest in the case of correlation pvalue) threshold was selected so that a majority of the algorithms, if not all, solved the graph. The smallest (largest in the case of correlation pvalue) threshold was selected so that at least one of the algorithms, but not all, solved the graph.
Conclusions
ES serves as a practical example of an innovative algorithm tailored to handle a difficult combinatorial problem by exploiting knowledge of the input space. It succeeds by exploiting properties of the graphs of interest, in this case the overlapping nature of maximum cliques. More broadly, these experiments underscore the importance of considering graph types when testing algorithms.
While MCC seems as if it should produce better results, in practice we find it not to be the case for two reasons. First, the vertices in an MCC may collectively be connected to a large portion of the rest of the graph, and so very little reduction in graph size takes place. And second, any reduction in graph size may be redundant with FPTstyle preprocessing rules already in place.
Contrast to random graphs
It would have probably been fruitless to test and design our algorithms around random graphs. (Yet practitioners do just that with some regularity.) In fact it has long been observed that the topology of graphs derived from real relationships differs drastically from the ErdösRényi random graph model introduced in [26]. Attempts to characterize the properties of real data graphs have been made, such as the notion of scalefree graphs, in which the degrees of the vertices follow a powerlaw distribution [27]. While work to develop the scalefree model into a formal mathematical framework continues [28], there remains no generally accepted formal definition. More importantly, the scalefree model is an inadequate description of real data graphs. We have observed that constructing a graph so the vertices follow a power law (scalefree) degree distribution, but where edges are placed randomly otherwise using the vertex degrees as relative probabilities for edge placement, still results in graphs with numerous small disjoint maximum cliques. For instance, constructing graphs with the same degree distribution as each of the 75 biological graphs in our original testbed resulted in maximum clique sizes no greater than 5 for even the highest density graphs. Compare this to maximum clique sizes that ranged into hundreds of vertices in the corresponding biological graphs. Other metrics have been introduced to attempt to define important properties, such as cluster coefficient and diameter. Collectively, however, such metrics remain inadequate to model fully the types of graphs derived from actual biological data. The notions of maximum clique cover and essential vertices stem from the observation that transcriptomic data graphs tend to have one very large highlyconnected region, and most (very often all) of the maximum cliques lie in that space. Furthermore, there tends to be a great amount of overlap between maximum cliques, perhaps as a natural result of gene pleiotropism. Such overlap is key to the runtime improvement achieved by the ES algorithm.
Future research directions
Our efforts with MCE suggest a number of areas with potential for further investigation. A formal definition of the class of graphs for which ES achieves runtime improvements may lead to new theoretical complexity results, perhaps based upon parameterizing by the amount of maximum clique overlap. Furthermore, such a formal definition may form the basis of a new model for real data graphs. We have noted that the number of disjoint maximum cliques that can be extracted provides an upper bound on the size of an MCC. If we parameterize by the maximum clique size and the number of maximum cliques, does an FPT algorithm exist? In addition, formal mathematical results may be achieved on the sensitivity of the number of maximum cliques to small changes in the graph.
For the subset cover problem, we have noted that it is $\mathcal{N}\mathcal{P}$hard by a simple reduction from hitting set. But in the context of MCE we have subsets all of the same size. It may be that this alters the complexity of the problem, or that one can achieve tighter complexity bounds when parameterizing by the subset size. Alternately, consider the problem of finding the minimum subset cover given a known minimum hitting set. The complexity of this tangential problem is not at all clear, although we conjecture it to be $\mathcal{N}\mathcal{P}$complete in and of itself. Lastly, as a practical matter, exploring whether an algorithm that addresses the memory issues of the subset enumeration algorithm presented in [13] and improved in [14] may also prove fruitful. As we have found here, it may well depend at least in part on the data.
Declarations
Acknowledgements
This article has been published as part of BMC Bioinformatics Volume 13 Supplement 10, 2012: "Selected articles from the 7th International Symposium on Bioinformatics Research and Applications (ISBRA'11)". The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/13/S10.
This research was supported in part by the National Institutes of Health under grants R01MH074460, U01AA016662 and R01AA018776, and by the U.S. Department of Energy under the EPSCoR Laboratory Partnership Program. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus [25] and are accessible through GEO Series accession number GSExxx http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSExxx.
Authors’ Affiliations
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